AUTOMATION HISTORY

World of Robots – это динамичный тактический онлайн-шутер про гигантских боевых роботов, где вас ждут масштабные PvP-баталии и безграничные возможности для стратегического превосходства. Погрузитесь в мир высокотехнологичных сражений, управляя многотонными машинами с мощным арсеналом. Разрабатывайте уникальные тактики, экспериментируя с разными типами оружия и модификациями роботов. Объединяйтесь с игроками со всего мира, создавайте непобедимые кланы и доминируйте на глобальной арене. С каждым обновлением World of Robots становится еще интереснее – новые карты, виды вооружения и спецэффекты делают каждую битву неповторимой. Реалистичная физика разрушений и детализированная графика усиливают погружение в эпичные сражения будущего. Особенности World of Robots:

Merge Robots – насладитесь созданием крутых и мощных роботов, оснащайте их самыми прогрессивными технологиями и зарабатывайте на этом миллиарды. Постройте настоящую роботическую империю и станьте футуристическим королем нашего времени! Соревнуйтесь в другими пользователями, докажите всем, что вы тут самый главный и богатый. Все очень просто: чтобы создать нового робота, просто соедините два одинаковых. Продолжайте кликать и создавать новых роботов, производству нельзя стоять, иначе другие геймеры станут лидерами рынка. Не забывайте каждый день крутить колесо с призами, ведь в наше время без инвестиций – никуда! Скорее скачивайте Merge Robots и начинайте развивать свой бизнес уже сегодня! Особенности игры Merge Robots:

Android Ayuda » Aplicaciones » Recomendadas 12 minutos Limpiar bien los ventanales, espejos y mamparas de casa puede ser un auténtico quebradero de cabeza, sobre todo cuando hay que asomarse a la parte exterior o lidiar con manchas que no salen ni a la de tres. En ese punto, los robots limpiacristales con app para Android son una pequeña salvación: automatizan el trabajo, se adhieren con succión al vidrio y te dejan supervisar todo desde el móvil sin tener que jugártela en una escalera. Si has llegado hasta aquí buscando cuáles son los mejores modelos compatibles con tu smartphone, estás en el sitio adecuado. Hemos reunido lo más destacado de las guías y pruebas de referencia para que puedas comparar, entender las diferencias reales entre ellos y comprar con cabeza. Encontrarás modelos con app Android, alternativas sin app pero muy solventes, pros y contras, y trucos de uso que marcan la diferencia en el resultado final. Estos dispositivos se adhieren a la superficie mediante un potente sistema de succión y recorren el cristal con rutas en zigzag u otros patrones, mientras sus mopas (en seco o en húmedo) arrastran la suciedad. La app para Android aporta control remoto, selección de modos, supervisión y, en algunos casos, ajustes finos como potencia o frecuencia de pulverización. Además del motor de succión, hay dos elementos clave: el paño o mopa (suelen venir varias de repuesto) y la seguridad. Los mejores equipos incorporan cuerda/cordón de seguridad y batería de respaldo para evitar caídas si se corta la luz, un detalle que conviene exigir en cualquier ventana con altura. Al evaluar estos robots, conviene ir más allá de las cifras y fijarse en lo que realmente cambia la experiencia. En las pruebas prácticas de los diferentes análisis consultados se valoraron cuatro aspectos: diseño y calidad de construcción (incluida la forma, cuadrada o alargada), rendimiento de limpieza, seguridad y facilidad de uso. En limpieza cuenta la combinación de modos, el tipo de mopa y el acabado tras una o varias pasadas. También los pulverizadores, cuando existen, que aportan constancia de líquido en la zona de trabajo. En seguridad, aunque el funcionamiento sea autónomo, se recomienda usarlo estando presente, con cuerda de seguridad colocada y batería de emergencia para cortes de corriente. La facilidad de uso depende del control (mando, app o manual), la claridad de los modos y lo intuitivo que resulta ponerlo a trabajar. Si buscas un primer robot, la app para Android puede simplificar el manejo y permitir rutas precisas sin complicarte. Un viejo conocido por su relación calidad-precio. Su diseño cuadrado le permite llegar mejor a las esquinas y cubrir la superficie de forma uniforme. Destaca por su velocidad: alrededor de 2,5 minutos por m², cuando otros tardan entre 4 y 5 minutos, lo que se nota mucho si tienes ventanales. Incluye dos tipos de mopas: unas para limpiar en seco (muy recomendables como primera pasada si hay polvo incrustado) y otras para húmedo, que puedes rociar con agua o limpiacristales. Se maneja desde mando o app móvil, y si lo enciendes directamente en el botón arranca en modo automático. Viene con los sistemas de seguridad esperables: batería de respaldo y cable de seguridad. El único punto menos cómodo es tener que alternar mopas para seco y mojado, aunque así logras la limpieza completa. Diseño cuadrado, cinco programas automáticos y control por mando o app. En pruebas reales, el doble spray de agua dosifica poco a poco para que siempre limpie con líquido fresco. Calcula la ruta, detecta límites y se apaga al terminar. Llega bastante bien a las esquinas y la velocidad de trabajo es más que aceptable para mantener la casa al día. En seguridad, cable resistente y sistemas adicionales para contingencias como cortes de luz. Funciona también en azulejos del baño con un resultado sorprendentemente bueno. Como contra, si los marcos son muy finos, alguna vez puede no detectarlos perfectamente. Ficha técnica destacada: 24,6 × 25,2 × 9,2 cm, 1,95 kg, 5 modos y 4 métodos de seguridad. Otro modelo conectado de Cecotec con cuatro programas automáticos y tecnología para detenerse al finalizar. Se controla con mando o desde la app y limpia cristales de cualquier grosor, azulejos y superficies lisas en interior y exterior. Incluye varios sistemas de seguridad anticaídas para aportar tranquilidad en ventanas altas. Este modelo combina navegación inteligente, cálculo de ruta, detección de límites y pulverización automática para una limpieza profunda. Incluye cinco modos, sistema anticaídas y mopas de microfibra de buena calidad. Resulta una opción sólida si quieres un Cecotec con app y buenas funciones automáticas. Compacto y alargado, con app y conexión WiFi (además del mando). Dispone de dos niveles de pulverización, modo de «limpieza de manchas» que se entretiene en lo realmente complicado y un depósito de 60 ml. Su succión de 3.200 Pa y la app (espejo del mando) permiten dirigirlo justo a la zona que quieras. Silencioso para su categoría (70 dB) y con batería de emergencia de unos 30 minutos por si se va la luz. Como consejo de uso, no humedezcas demasiado la mopa o perderá agarre. Y si hay mucho polvo o arena, realiza una primera pasada en seco para no rayar el cristal. Medidas: 27 × 13,6 × 8 cm; 1,15 kg; 2 modos; cable de seguridad de 5 m. Un robot de gama alta centrado en la experiencia y la seguridad. Ofrece succión de 2.800 Pa para adherirse firmemente, protección ante cortes de electricidad y compensación de presión atmosférica. Se controla desde el propio dispositivo o mediante su app para smartphone Android/iOS. En limpieza, integra WinSlam 3.0 con planificación inteligente y 3 modos adaptativos. La pulverización cruzada con dos niveles de flujo ayuda a desincrustar sin rayar, y la bayeta rodea el contorno para dejar un acabado homogéneo. Es un dispositivo pensado para quien quiere resultados muy consistentes con supervisión desde el móvil. Con forma cuadrada, cable de alimentación largo (alrededor de 7 metros), peso contenido y navegación inteligente con IA. Calcula el tamaño de la ventana, elige la ruta más eficiente y puede limpiar 1 m² en aproximadamente 2,5 minutos. Dispone de batería de emergencia (SAI) y cuerda de seguridad. Se maneja con control remoto o app para Android/iOS, por lo que encaja perfecto si quieres control desde el móvil. Un modelo avanzado con app (iOS y Android), 3 modos automáticos y limpieza de múltiples superficies: ventanas interiores y exteriores, azulejos, mármol y más. Cuenta con depósito de 30 ml para tu limpiacristales o vinagre y avisos sonoros si se corta la corriente eléctrica. Es un «todo terreno» para quien busca versatilidad y control por smartphone. Aunque aquí priorizamos los que incluyen app para Android, hay robots muy competentes que, aun sin app, merecen mención por rendimiento o por su relación calidad-precio; y también existen apps para amas de casa que complementan su uso. Pueden ser una gran compra si lo del móvil no es imprescindible para ti. Ideal para mantenimiento diario. Similar en diseño a otros alargados, trae 12 mopas de repuesto y exige humedecerlas y escurrirlas antes de empezar. Se controla desde el mando, con potencia de succión de 3.800 Pa y opción de una o dos pasadas. Tiene cable de seguridad de 5 m y batería de emergencia de unos 20 minutos. Si llevas tiempo sin limpiar, conviene una primera pasada manual para quitar costra. Dimensiones: 29 × 13,5 × 8,5 cm; 1,14 kg; 3 modos. Muy ligero y compacto (unos 8,5 cm de alto), cabe donde otros no, como en ventanas con doble cristal o barrotes. Compatible con ventanas sin marco gracias a sus sensores de borde. No incorpora pulverización automática ni app, y se maneja con mando. Muy interesante si necesitas un equipo que entre en huecos ajustados. Un buen ayudante para cristales medianamente sucios. Trae 12 mopas, 3 modos preestablecidos y dos depósitos de agua (25 ml cada uno) con pulverización en la dirección de avance. Buena adherencia, batería de emergencia de 30 minutos y cordón de seguridad, aunque este último es algo más endeble que en otros. Funciona mejor en vidrio que en azulejos y puede dejar ligeras marcas de mopa. 29 × 14,5 × 10 cm; 900 g; 3 modos. Versión sin app del modelo de Create, capaz de limpiar hasta 1 m² en unos 4 minutos con rutas en zigzag. Incluye 12 mopas lavables, cable de seguridad largo y batería de respaldo de 30 minutos. Una compra muy sensata si buscas rapidez y seguridad sin complicarte con móviles. Este modelo destaca por su base: actúa como controlador, estación de carga, estabilizador y maletín de transporte. Es una opción orientada a quien valora la experiencia de uso y el orden, con la tranquilidad que aporta una estación pensada para todo el ciclo del robot. Antes de empezar, retira polvo y arena de la superficie. En cristales con mucha suciedad, haz una primera pasada en seco (o manual si hace mucho que no se limpian). Así evitas rayar el vidrio y ayudas a que el robot deslice mejor. Si tu robot pulveriza o humedeces las mopas, no te pases con el líquido. Demasiada humedad reduce la adherencia de la succión y puede provocar resbalones. Mejor mojar poco y repetir pasada si hace falta. En ventanas exteriores o a cierta altura, coloca siempre el cordón de seguridad y usa la batería de respaldo como última barrera ante cortes de suministro. Aunque muchos robots cuentan con medidas de seguridad, lo ideal es estar presente mientras trabajan. Ojo a los marcos muy finos: algunos modelos pueden detectarlos peor y necesitar un empujoncito manual al principio. En azulejos, ciertos robots se mueven con menos precisión que en vidrio y repiten zonas; es normal, el agarre varía según la textura. Si el robot ofrece modos (una o dos pasadas), úsalo a tu favor: dos pasadas seguidas suelen dejar el cristal impecable cuando hay manchas incrustadas. Y si incorpora “limpieza de manchas”, actívalo para que se detenga donde hace falta insistir. ¿Cómo funciona un robot limpiacristales? Se fija al cristal por succión y recorre la superficie con rutas predefinidas, usando mopas para arrastrar la suciedad en seco y/o húmedo. Los modelos con pulverización aplican líquido durante el proceso. La mayoría detecta bordes y límites para no salirse y cuentan con cable y batería de emergencia como medidas de seguridad. ¿Qué ventajas tiene que lleve app para Android? Más control y comodidad: eliges modos, gestionas pasadas, diriges el robot a zonas concretas y supervisas sin depender del mando. Si vas a usarlo con frecuencia, la app agiliza y hace más predecibles los resultados. ¿Todos limpian esquinas por igual? No. Los de formato cuadrado suelen llegar mejor a las esquinas, mientras que los alargados pueden moverse con mayor soltura en ciertas situaciones. Si tus marcos son muy marcados o buscas rematar esquinas, prioriza un diseño cuadrado. ¿Sirven para azulejos o mármol? Sí, varios modelos probados trabajan correctamente en azulejos y algunos incluso en mármol, aunque en superficies no vítreas el movimiento puede ser menos preciso. Revisa que el fabricante indique compatibilidad con superficies lisas no vítreas. ¿Qué pasa si se va la luz? Los mejores incluyen batería de respaldo de 20–30 minutos para mantenerse adheridos y evitar caídas. Usa siempre el cordón de seguridad, especialmente en exterior o a gran altura. Si quieres cristales impecables con el mínimo esfuerzo y control total desde el móvil, los robots con app para Android como Mamibot W120-T, Cecotec (1290, 870, 1390), Create Wipebot Pro, Ecovacs Winbot W1 Pro o Schbot WindX1 son apuestas muy fiables. Elige potencia suficiente, buenas medidas de seguridad, modos útiles y una app clara; aplica los consejos de uso (pasada en seco, poca humedad, cuerda puesta) y notarás la diferencia en menos tiempo del que imaginas. Comparte esta lista de robots limpiacristales para que más usuarios sepan cuál elegir.

Latest AI Amazon Apps Biotech & Health Climate Cloud Computing Commerce Crypto Enterprise EVs Fintech Fundraising Gadgets Gaming Google Government & Policy Hardware Instagram Layoffs Media & Entertainment Meta Microsoft Privacy Robotics Security Social Space Startups TikTok Transportation Venture Staff Events Startup Battlefield StrictlyVC Newsletters Podcasts Videos Partner Content TechCrunch Brand Studio Crunchboard Contact Us Many companies are focused on building robots, or the hardware components to help them move, grip objects, or interact with the world around them. Silicon Valley-based OpenMind is focused under the hood. OpenMind is building a software layer, OM1, for humanoid robots that acts as an operating system. The company compares itself to being the Android for robotics because its software is open and hardware agnostic. Stanford professor Jan Liphardt, the founder of OpenMind, told TechCrunch that humanoids and other robots have been around and able to do repetitive tasks for decades. But now that humanoids are being developed for use cases that require more human-to-machine interactions, like having a humanoid in your home, they need a new operating system that thinks more like a human. “All of a sudden, this world is opening where machines are able to interact with humans in ways I’ve certainly never before seen,” Liphardt said. “We’re very much believers here that it’s not just about the humans, but we really think of ourselves as a company that is a collaboration between machines and humans.” OpenMind unveiled on Monday a new protocol called FABRIC that allows robots to verify identity and share context and information with other robots. Unlike humans, machines can learn almost instantly, Liphardt said, which means giving them a better way to connect to other robots will allow them to more easily train and absorb new information. Liphardt gave the example of languages and how robots could connect to each other and share data on how to speak different languages, which would help them better interact with more people without having to be taught each language by a human directly. “Humans take it for granted that they can interact with any other human on Earth,” Liphardt said. “Humans have built a lot of infrastructure around us that allows us to trust other people, call them, text them, and interact and coordinate and do things together. Machines, of course, are going to be no different.” OpenMind was founded in 2024 and is gearing up to ship its first fleet of 10 OM1-powered robotic dogs by September. Liphardt said that he’s a big believer in getting the tech out there and iterating on it after the fact. “We full well expect all the humans that will be hosting these quadrupeds, they’ll come back with a long list of things they didn’t like or they want, and then it’s up to us to very, very quickly iterate and improve the machines,” he said. The company also recently raised a $20 million funding round led by Pantera Capital, with participation from Ribbit, Coinbase Ventures, and Pebblebed, among other strategic investors and angel investors. Now, the company is focused on getting its tech into people’s homes and starting to iterate on the product. “The most important thing for us is to get robots out there and to get feedback,” Liphardt said. “Our goal as a company is to do as many of these tests as we can, so that we can very rapidly identify the most interesting opportunities where the capabilities of the robots today are optimally matched against what humans are looking for.” Topics Senior Reporter, Venture Becca is a senior writer at TechCrunch that covers venture capital trends and startups. She previously covered the same beat for Forbes and the Venture Capital Journal. You can contact or verify outreach from Becca by emailing rebecca.szkutak@techcrunch.com. Plan ahead for the 2026 StrictlyVC events. Hear straight-from-the-source candid insights in on-stage fireside sessions and meet the builders and backers shaping the industry. Join the waitlist to get first access to the lowest-priced tickets and important updates. OpenAI bets big on audio as Silicon Valley declares war on screens The phone is dead. Long live . . . what exactly? Meta just bought Manus, an AI startup everyone has been talking about You’ve been targeted by government spyware. Now what? Sauron, the high-end home security startup for ‘super premium’ customers, plucks a new CEO out of Sonos The Google Pixel Watch 4 made me like smartwatches again NY Governor Hochul signs bill requiring warning labels on ‘addictive’ social media © 2025 TechCrunch Media LLC.

Google DeepMind ficha al ex CTO de Boston Dynamics para liderar su sistema operativo universal para robots. Aaron Saunders, que lleva 23 años en la compañía creadora del famoso Atlas, asume el puesto de vicepresidente de ingeniería de hardware. El movimiento confirma que la filial de IA va en serio con su proyecto Gemini, el famoso 'Android de la robótica'.RobotsQué robot aspirador comprarHistoria de los robots aspiradoresXiaomi CyberdogComo recoge WinBuzzer, Saunders se incorporó a Boston Dynamics en 2018 y fue ascendido a CTO en 2021. Allí se curtió desarrollando los prototipos más avanzados de la industria, desde los cuadrúpedos Spot hasta el humanoide Atlas que nos dejó a todos con la boca abierta por sus piruetas imposibles.Un cerebro universal para cualquier cuerpo robóticoDemis Hassabis, CEO de DeepMind, no se anda con rodeos: quiere crear un "Android para robots". En lugar de fabricar sus propios robots, la idea es desarrollar una base de IA que pueda controlar cualquier configuración de hardware. ¿En qué se traduce esto? En crear un sistema que funcione en cualquier configuración corporal sin importar si es humanoide, cuadrúpedo o con ruedas.Esta aproximación busca separar la inteligencia del chasis físico, evitando los quebraderos de cabeza de fabricar hardware mientras extiende sus modelos de IA por toda la industria. El planteamiento replica el modelo Android: Google proporciona el cerebro, otros fabrican el cuerpo.Sin embargo, el fichaje de Saunders tiene su gracia. A pesar de todo el discurso sobre software, incorporar a un veterano constructor sugiere que Google adopta una táctica similar a Pixel: crear hardware propio para demostrar lo que puede hacer su software. Ya hemos visto como Google desarrolla robots con IA que pueden pensar antes de actuar y hacer tareas tan cotidianas como clasificar ropa.La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos Como recoge WinBuzzer, Saunders se incorporó a Boston Dynamics en 2018 y fue ascendido a CTO en 2021. Allí se curtió desarrollando los prototipos más avanzados de la industria, desde los cuadrúpedos Spot hasta el humanoide Atlas que nos dejó a todos con la boca abierta por sus piruetas imposibles.Un cerebro universal para cualquier cuerpo robóticoDemis Hassabis, CEO de DeepMind, no se anda con rodeos: quiere crear un "Android para robots". En lugar de fabricar sus propios robots, la idea es desarrollar una base de IA que pueda controlar cualquier configuración de hardware. ¿En qué se traduce esto? En crear un sistema que funcione en cualquier configuración corporal sin importar si es humanoide, cuadrúpedo o con ruedas.Esta aproximación busca separar la inteligencia del chasis físico, evitando los quebraderos de cabeza de fabricar hardware mientras extiende sus modelos de IA por toda la industria. El planteamiento replica el modelo Android: Google proporciona el cerebro, otros fabrican el cuerpo.Sin embargo, el fichaje de Saunders tiene su gracia. A pesar de todo el discurso sobre software, incorporar a un veterano constructor sugiere que Google adopta una táctica similar a Pixel: crear hardware propio para demostrar lo que puede hacer su software. Ya hemos visto como Google desarrolla robots con IA que pueden pensar antes de actuar y hacer tareas tan cotidianas como clasificar ropa.La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos Demis Hassabis, CEO de DeepMind, no se anda con rodeos: quiere crear un "Android para robots". En lugar de fabricar sus propios robots, la idea es desarrollar una base de IA que pueda controlar cualquier configuración de hardware. ¿En qué se traduce esto? En crear un sistema que funcione en cualquier configuración corporal sin importar si es humanoide, cuadrúpedo o con ruedas.Esta aproximación busca separar la inteligencia del chasis físico, evitando los quebraderos de cabeza de fabricar hardware mientras extiende sus modelos de IA por toda la industria. El planteamiento replica el modelo Android: Google proporciona el cerebro, otros fabrican el cuerpo.Sin embargo, el fichaje de Saunders tiene su gracia. A pesar de todo el discurso sobre software, incorporar a un veterano constructor sugiere que Google adopta una táctica similar a Pixel: crear hardware propio para demostrar lo que puede hacer su software. Ya hemos visto como Google desarrolla robots con IA que pueden pensar antes de actuar y hacer tareas tan cotidianas como clasificar ropa.La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos Esta aproximación busca separar la inteligencia del chasis físico, evitando los quebraderos de cabeza de fabricar hardware mientras extiende sus modelos de IA por toda la industria. El planteamiento replica el modelo Android: Google proporciona el cerebro, otros fabrican el cuerpo.Sin embargo, el fichaje de Saunders tiene su gracia. A pesar de todo el discurso sobre software, incorporar a un veterano constructor sugiere que Google adopta una táctica similar a Pixel: crear hardware propio para demostrar lo que puede hacer su software. Ya hemos visto como Google desarrolla robots con IA que pueden pensar antes de actuar y hacer tareas tan cotidianas como clasificar ropa.La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos Sin embargo, el fichaje de Saunders tiene su gracia. A pesar de todo el discurso sobre software, incorporar a un veterano constructor sugiere que Google adopta una táctica similar a Pixel: crear hardware propio para demostrar lo que puede hacer su software. Ya hemos visto como Google desarrolla robots con IA que pueden pensar antes de actuar y hacer tareas tan cotidianas como clasificar ropa.La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos La experiencia de Saunders viene como anillo al dedo para resolver el problema de simulación a realidad, ese escollo donde los robots que funcionan perfectamente en simulaciones se estrellan contra la realidad física. Su experiencia práctica con actuación hidráulica y eléctrica puede acelerar el salto de los laboratorios a aplicaciones reales.El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos El fichaje llega justo cuando el hardware robótico se está abaratando a marchas forzadas, liderado por fabricantes chinos como Unitree. Esta empresa se ha convertido en el mayor proveedor de sistemas cuadrúpedos entregando 10 veces más unidades que la competencia en 2023-2024, algo parecido a como Boston Dynamics rompió esquemas con sus robots humanoides eléctricos que parecían CGI.Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos Mientras, la industria muestra distintas formas de funcionar: Tesla va a lo suyo con un ecosistema cerrado para Optimus, Meta juega la carta del código abierto con V-JEPA 2, un modelo que enseña sentido común físico a los robots. Como ya demostró Google con robots jugando ping-pong, la carrera ya no va de contratar investigadores, sino de fichar a gente que sepa hacer robots que funcionen de verdad.El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos El movimiento deja claro que DeepMind apuesta por el "cerebro" como la parte más valiosa del robot. Pero reconoce que necesita entender el "cuerpo" para que su software no se quede corto. Esta mezcla entre la experiencia física de Saunders y la IA avanzada de Google podría marcar el momento en que los robots dejen de ser prototipos de laboratorio para convertirse en herramientas realmente útiles.Únete a la conversación Si estabas pensando en comprarte el iPhone 18 en 2026, tengo malas noticias para ti Sigue siendo un referente: 512 GB, Snapdragon 8 Gen 3, carga de 90 W y Android 16 por 358 euros Carta a los Reyes Magos: esta es mi lista de deseos como fan de Apple para el 2026 200 megapíxeles y batería para dos días: el nuevo "Pro Max" de OPPO quiere que te olvides de la gama alta Lleva la magia del 4K con WiFi 6 y sonido Bluetooth a cualquier pared y disfruta tus pelis y series como nunca con este proyectoriRobot, la empresa detrás de los robots Roomba, se declara en bancarrota: ¿qué significa para ti como usuario? El padrino de la robótica dice que la fiebre de los humanoides es una burbuja condenada al fracaso El robot más pequeño del mundo es capaz de nadar, sentir la temperatura y hasta "pensar" Así es el robot autónomo más pequeño del mundo que un día podría salvarte la vida Saben correr, pero no trabajar: los creadores de robots humanoides admiten que el 'hype' se nos ha ido de las manos

El grupo formado por Indar Kartera, el Gobierno Vasco, la Fundación BBK y Teknei compra Ayesa Digital El precio de la memoria DDR5 subirá otro 45% en 2026 Meta se queda con la startup Manus para integrar agentes de IA en sus redes sociales SoftBank avanza en inversión en infraestructura para IA con la compra de DigitalBridge Microsoft agiliza la protección de archivos con el BitLocker acelerado por hardware Cómo arreglar Windows 11 y mejorar su cuota de mercado Los deepfakes de 2026 harán imposible distinguir la realidad Las 10 tendencias tecnológicas más destacadas para el sector TI en 2026 AMD Threadripper 9980X y Threadripper 9970X análisis Principales tendencias que afectarán a infraestructuras y operaciones en 2026 «LG dedica el 75% de su I+D a tecnología para las empresas» «La salud emocional del CEO impacta directamente en la salud del negocio» «El cambio no se impone, se inspira y se construye con equipos comprometidos» «La cultura se come a la estrategia en el desayuno…. y al compliance de postre» “La IA convierte la cadena de suministro en un sistema anticipativo” ¿Qué empresas hacen una mayor aportación socioeconómica al PIB español? Nvidia da un respiro a inversores y firmas Big Tech…, por ahora El uso ético de la IA en proyectos de I+D europeos Así es como las GPU han reemplazado definitivamente a las CPU como las protagonistas indiscutibles de la computación La digitalización empresarial aporta más del 20% del PIB español IA, soberanía digital y open source: más allá del ‘hype’ Tienes una cita el 20/11 en el Tour Tecnológico @aslan 2025 en Málaga Dell Technologies Forum 2025: la tecnología de vanguardia de Dell, de nuevo en Madrid LG España celebra su evento «Juntos» para clientes B2B Barcelona reune a CIOs y directivos tecnológicos en la IV edición del Congreso LiceoTIC La seguridad documental: un pilar esencial para las organizaciones modernas Controla la calidad de los datos con DocuWare Validation Smart Connect: integra tus sistemas empresariales con DocuWare Smart Connect: Integración avanzada de aplicaciones con DocuWare Workflow Expression Parser, personalizar DocuWare sin programar Publicado el por OpenMind está desarrollando una capa de software, OM1, que actúa como sistema operativo para la próxima generación de robots humanoides, y ayer dio un nuevo paso con la presentación de un nuevo protocolo llamado FABRIC que permite a los robots verificar la identidad y compartir contexto e información con otros de su especie. Muchas empresas se centran en la construcción de robots o de los componentes de hardware que les permiten moverse, sujetar objetos o interactuar con el mundo que los rodea. OpenMind, una firma especializada con sede en Silicon Valley, considerada a sí misma el Android de la robótica porque su software es abierto y no depende del hardware, se centra en el desarrollo interno. Jan Liphardt, profesor de Stanford y fundador de OpenMind, ha explicado que los humanoides y otros robots llevan décadas existiendo y realizando tareas repetitivas. Pero ahora que se están desarrollando humanoides para casos de uso que requieren más interacciones entre humanos y máquinas, como tener un humanoide en casa, necesitan un nuevo sistema operativo que piense más como un humano. «De repente, se abre un mundo donde las máquinas pueden interactuar con los humanos de maneras nunca antes vistas», dijo Liphardt. «Aquí creemos firmemente que no se trata solo de los humanos, sino que nos consideramos una empresa que es una colaboración entre máquinas y humanos”. El nuevo protocolo de OpenMind permite a los robots verificar la identidad y compartir contexto e información con otros robots. A diferencia de los humanos, las máquinas pueden aprender casi instantáneamente, dijo Liphardt, lo que significa que darles una mejor manera de conectarse con otros robots les permitirá entrenarse más fácilmente y absorber nueva información. Liphardt puso el ejemplo de los idiomas y de cómo los robots podrían conectarse entre sí y compartir datos sobre cómo hablar diferentes idiomas, lo que les ayudaría a interactuar mejor con más personas sin tener que aprender cada idioma directamente de un humano. «Los humanos damos por sentado que podemos interactuar con cualquier otro ser humano en la Tierra», dice el investigador. «Hemos construido una gran infraestructura a nuestro alrededor que nos permite confiar en otras personas, llamarlas, enviarles mensajes, interactuar, coordinarnos y hacer cosas juntos. Las máquinas, por supuesto, no serán la excepción«. La nueva era de la robótica está en marcha y el gran objetivo de OpenMind es llevar su tecnología a los hogares. Para ello, recaudó recientemente 20 millones de dólares en una ronda de financiación liderada por Pantera Capital, con la participación de Ribbit, Coinbase Ventures y Pebblebed, entre otros inversores estratégicos. «Lo más importante para nosotros es lanzar robots y recibir retroalimentación», dijo Liphardt. «Nuestro objetivo como empresa es realizar tantas pruebas como sea posible para identificar rápidamente las oportunidades más interesantes donde las capacidades de los robots actuales se ajusten óptimamente a lo que buscan los humanos«. Microsoft 365 desactivará en enero varias funciones de Office si la suite no está actualizada La consultora knowmad mood compra la agencia creativa digital Buzz Colaboro en medios profesionales y de consumo de TPNET: MCPRO, MuySeguridad, MuyCanal y MuyComputer Los deepfakes de 2026 harán imposible distinguir la realidad Las 10 tendencias tecnológicas más destacadas para el sector TI en 2026 NVIDIA despliega la RTX PRO 5000 con 72 GB de memoria «El cambio no se impone, se inspira y se construye con equipos comprometidos» “La IA convierte la cadena de suministro en un sistema anticipativo” La IA seguirá siendo un desafío para la ciberseguridad en 2026 Los deepfakes de 2026 harán imposible distinguir la realidad NVIDIA compra activos de la startup de chips para IA Groq Telefónica Tech vende a Hiberus sus divisiones de negocio en Colombia, México y Chile Crece el robo de cuentas de Microsoft 365 Copyright © Total Publishing Network S.A. 2026 | Todos los derechos reservados

Androidsis » Juegos Android 10 minutos Si te flipan las peleas entre máquinas gigantes, en Android tienes un buen puñado de propuestas que combinan acción directa, estrategia y progresión. No hablamos solo de disparar a lo loco: hay tácticas, construcción de bases, plantillas que se mejoran y combates con diferentes ritmos según el juego. La gracia está en encontrar el título que encaje contigo, ya busques batallas en línea, duelos 3v3 o la fantasía de liderar héroes de una saga mítica. En esta guía te presentamos una selección variada con opciones que han conquistado a miles de jugadores. Verás desde campos de batalla con mechas colosales y PvP sin descanso, hasta un juego de estrategia oficial de Transformers o un arcade de lucha por equipos con estética cell‑shading. Además de repasarlos al detalle, te explicamos qué los hace únicos para que elijas con cabeza. El encanto de estos títulos está en que mezclan sensaciones muy distintas: desde la adrenalina del enfrentamiento directo hasta la planificación al milímetro. Hay propuestas centradas en el multijugador competitivo en tiempo real, otras que apuestan por la construcción y defensa de tu base, y algunas que convierten cada pelea en un espectáculo de combos táctiles. El encanto de estos títulos está en que mezclan sensaciones muy distintas: desde la adrenalina del enfrentamiento directo hasta la planificación al milímetro. Hay propuestas centradas en el multijugador competitivo en tiempo real, otras que apuestan por la construcción y defensa de tu base, y algunas que convierten cada pelea en un espectáculo de combos táctiles. También verás sistemas de progresión profundos. La mayoría permiten mejorar tu plantilla: incrementas velocidad de movimiento, maniobrabilidad, potencia de fuego y blindaje, desbloqueas módulos o cartas de poder, y personalizas la pinta con camuflajes y calcas. Esa sensación de crecimiento continuo es parte clave del enganche. Otro punto diferencial es la ambientación. Desde robots inspirados en dioses, gladiadores o samuráis hasta unidades icónicas como Autobots y Decepticons. Todo ello apoyado por gráficos 3D actuales y efectos llamativos, e incluso estilos artísticos cell‑shaded con acabado de consola en el móvil. Por último, la comunidad. Estos juegos brillan cuando participan amigos o te mides a jugadores de todo el mundo. Encontrarás chat integrado, torneos y rankings para que cada sesión tenga objetivos claros y un plus de picante competitivo. En este título saltas directo a campos de batalla donde pilotas mechas de gran tonelaje frente a jugadores de todo el planeta. El objetivo es claro: demostrar que eres el comandante más listo, veloz y contundente. Cada ronda es pura tensión, con enfrentamientos rápidos y decisiones que separan la victoria del desastre. La fantasía es potente: la humanidad ha aprendido a construir enormes guerreros mecánicos, auténticos «mech warrior» preparados para combates de alto voltaje. En este entorno, no todo es apretar el gatillo; influyen el posicionamiento, las rutas de flanqueo y esas maniobras con truco y pequeñas tretas tácticas que marcan la diferencia. Te lo repetirán una y otra vez: tu rival más peligroso puedes ser tú mismo si no controlas nervios y errores. Los robots de batalla presumen de una fuerza bruta al nivel de su acero. Se les atribuye una potencia descomunal, con detalles curiosos como la capacidad de almacenar energía cuando circula corriente y aprovecharla en el momento justo. Aunque suene técnico, en la práctica se traduce en golpes que cambian el signo de la partida. El crecimiento del piloto es constante. Puedes potenciar tu unidad en cuatro frentes clave: aumentas la velocidad para reposicionarte, afinas la maniobrabilidad para esquivar mejor, refuerzas el blindaje y subes la capacidad ofensiva. Cuanto más inviertes, más opciones reales de dominar el campo de batalla y encadenar puntuaciones altas. Jugar en compañía suma muchísimo. Invita a tus colegas y salid juntos a romper líneas rivales en estas guerras de robots gratuitas. Esa coordinación entre amigos, cuando cada uno cumple su rol, convierte cada partida en una historia memorable. Entre sus bazas principales están las partidas en línea repletas de ritmo. El PvP no te deja tomar aire: se siente ágil y «a cuchillo». Le acompañan gráficos 3D modernos que visten bien los escenarios y la chatarra volando, además de una buena colección de módulos para afinar tu bot al detalle. La escena social está muy trabajada. Puedes sumarte a comunidades de fans para enterarte de las últimas actualizaciones, competir en torneos con tablas de clasificación y coleccionar recompensas, compartiendo además tus logros en redes como Facebook para vacilar con estilo. Si te atraen las sensaciones fuertes, aquí tienes un «mech war» de los que hacen afición. Entra, aprieta el gatillo y demuestra que eres el mecha más temible del WWR. La combinación de tensión, progresión y juego en equipo lo convierte en un fijo del género en Android. ¿Te tira más la estrategia que el gatillo fácil? En Earth Wars, eliges bando –Autobots o Decepticons– y reclutas a tus robots favoritos para formar una escuadra demoledora. La base es el corazón del progreso: levantarla bien te permite resistir asaltos y preparar incursiones eficaces. La defensa no es cosa menor: reforzarás tus muros con lanzamisiles, torres de choque y torretas láser. Cada estructura cumple un papel, así que conviene pensar en sinergias y coberturas para que el enemigo no encuentre rutas fáciles. A la vez, planificarás tus ofensivas con unidades que se complementen. La campaña y los modos multijugador ofrecen ese loop clásico del género: mejoras edificios, desbloqueas robots, optimizas tiempos y vuelves al combate con ventaja. Es una aventura estratégica con mecánicas familiares para cualquiera que haya catado títulos similares en móvil, pero aquí con el plus del universo Transformers y sus personajes icónicos. Reunir el mejor equipo es media victoria. Con cada incorporación ajustas tu plantilla para cubrir roles –daño, aguante, apoyo– y, si escoge bien, verás cómo tus ataques pasan de «rascar» a arrasar defensas rivales. Esa progresión te anima a entrar a diario a por recursos, mejoras y nuevas piezas. Al final, Earth Wars es ideal si te gustan los ritmos pausados con decisiones a medio plazo. Construir, defender y atacar con cabeza tiene su magia, y más cuando lo haces al mando de Autobots o Decepticons de toda la vida. Si buscas espectáculo directo, combates por equipos y controles táctiles precisos, esta es tu parada. Aquí construyes una plantilla de robots hambrientos de pelea y te lanzas a la arena en duelos 3v3 de ritmo arcade. La curva es inmediata: aprendes con dos gestos y en minutos ya estás encadenando golpes. El sistema de control es todo toque y desliz: pulsas, deslizas y encadenas combos para triturar a los rivales. A medida que llenas la barra de energía, desbloqueas ataques especiales y remates capaces de cambiar una ronda. La tensión sube cuando decides si gastar ahora o reservar para un cierre espectacular. La plantilla es amplísima, con decenas de robots que beben de mil referencias: dioses, gladiadores, dragones, monjes, arsenales con patas, samuráis o ninjas. Hay más de 45 opciones únicas, y con variaciones adicionales que te permiten estrenar poderes y movimientos distintos para refrescar la experiencia. El progreso profundiza con mejoras, ascensos y combinaciones entre miembros del equipo. Existen conjuntos de sinergia que dan bonificaciones exclusivas; al dominar estas combinaciones, puedes reventar enfrentamientos que parecían fuera de tu alcance. Además, equipas cartas de poder y «overclocks» para subir daño y armadura, mejorar personajes y potenciar habilidades concretas. Es la vía perfecta para llevar a tu escuadrón a otro nivel y liberar la verdadera fuerza del acero en cada enfrentamiento. Visualmente, entra por los ojos: un estilo cell‑shaded con acabado de consola que luce de maravilla en móvil, efectos inéditos y entornos inmersivos que convierten cada golpe en una pequeña cinemática. Si te gusta jugar bonito, aquí vas servido. Es free‑to‑play y, como es habitual, incluye elementos de pago dentro de la app. Si no te interesan, puedes desactivar las compras integradas desde los ajustes del dispositivo y jugar sin gastar un euro. El diseño está pensado para progresar con o sin pasar por caja. ¿Quieres estar al día? El estudio mantiene comunicación activa con la comunidad: comparten noticias, actualizaciones, vídeos y consejos a través de su web y perfiles oficiales en redes como Facebook, Twitter o YouTube. Ideal para pillar trucos, adelantos de eventos y regalos. Piensa primero en el tipo de experiencia que te apetece. Si quieres batallas de alta tensión contra gente real y un meta de mejoras profundas, elige una propuesta con PvP continuo y módulos de personalización. Si prefieres construir y planificar, te encajará más una base que levantar y defender con calma. La progresión también importa. ¿Te motiva subir estadísticas, desbloquear camuflajes y presumir de bot? ¿O te seduce más coleccionar robots con estilos muy distintos y encontrar sinergias potentes? Ese gusto marcará la diferencia entre jugar dos días o engancharte una temporada larga. Fíjate en los controles y el ritmo. Los sistemas por toques con combos encadenados ofrecen una satisfacción instantánea, mientras que los títulos con manejo más táctico exigen colocación, lectura del mapa y timings. No hay mejor ni peor, solo lo que te pida el cuerpo en cada momento. Valora la parte social. Un buen chat, torneos periódicos y rankings activos hacen que tengas metas semanales y comunidad con la que compartir jugadas. Estos incentivos alargan la vida útil del juego y añaden un plus de motivación. Y no olvides revisar el modelo de monetización. La mayoría te deja avanzar gratis, y en algunos casos puedes bloquear las compras integradas si quieres una experiencia 100% sin microtransacciones. Lo importante es que el progreso te resulte justo y divertido. Entre propuestas de mechas con ritmo frenético, estrategia con sabor a Transformers y peleas 3v3 llenas de estilo, en Android tienes una oferta variada para todos los gustos. Si te llama la tensión del PvP con mejoras y comunidad, WWR es tu sitio; si lo tuyo es planificar y defender con tus robots favoritos, Earth Wars clava la esencia estratégica; si quieres espectáculo táctil con combos y plantillas enormes, Ultimate Robot Fighting te lo da hecho. Elijas lo que elijas, prepárate para acero, chispas y muchas horas de diversión.

MOSCOW, February 2. /TASS/. The use of humanoid robots at a cis-lunar station seems appropriate, Yevgeny Dudorov, executive director of the Android Technics research and production association, said in an interview with TASS. "Basically, when Russia will be working to set up a lunar orbital station, it would be logical to use humanoid robots inside pressurized compartments. The matter is that humans will rarely visit lunar bases and the cis-lunar station as they would be exposed to active space radiation there," Dudorov said. According to the Android Technics executive, in such an environment, which is highly hostile to human life, it would be better to use robots to handle potential emergency situations.

Android Ayuda » Juegos » Mejores juegos 10 minutos Si te apasiona ver chispas, acero y chatarra volar por los aires, los juegos de robots de combate para Android son un festín. En móviles tenemos desde duelos directos uno contra uno hasta propuestas de estrategia con bases, pasando por arenas PvP donde cada segundo cuenta y cada mejora puede decidir la victoria. En este recopilatorio nos centramos en lo que hemos visto en las páginas que mejor posicionan: títulos móviles como Transformers: Earth Wars, Mechangelion y Robot Crash Fight, recuerdos y comparativas alrededor de War Robots, y además una buena ración de referentes del género en consola/PC (Armored Core, MechWarrior, 13 Sentinels, Iron Harvest, Daemon X Machina, Gundam) que, aunque no son de Android, ayudan a entender el panorama de los mechas. Todo lo que sigue está reescrito con otras palabras, integrando toda la información disponible y aportando contexto para que elijas bien. Cuando hablamos de mechas en el móvil, podemos referirnos a varias cosas: duelos de robots a puñetazo limpio con controles simples, arenas PvP con ritmo alto y progresión, o títulos de estrategia con construcción de base en los que diseñar tu defensa y preparar ataques sincronizados. En Android conviven estos formatos y es importante saber qué te apetece antes de instalar. Una categoría muy habitual en smartphones es la de lucha 1v1 con mejoras, donde el núcleo es encadenar golpes, esquivas y habilidades de forma intuitiva. Aquí brilla la sensación de contacto, la variedad de movimientos y la progresión de piezas y armas que vas desbloqueando. Otra vertiente es la de estrategia y gestión, en la que formar equipos icónicos y planificar ataques o defensas da más peso a la táctica que al reflejo. La clave pasa por el equilibrio entre unidades, torretas y economía de recursos, algo que funciona muy bien en sesiones cortas. Finalmente, también hay espacio para la arena competitiva tipo PvP, donde quien mejor ajusta su configuración y sabe leer el mapa suele salir con ventaja. Son experiencias que premian la constancia y el reto online continuo. De entre las webs analizadas emergen tres propuestas móviles claras: Transformers: Earth Wars, la frenética Mechangelion – Robot Fighting y el competitivo Robot Crash Fight. A ello se suma la eterna pregunta: ¿sigue reinando War Robots en el PvP de mechas o le han salido rivales que lo superan? Si te tira la estrategia y el universo de Optimus y Megatron, aquí se junta lo mejor de ambos mundos: puedes reunir un escuadrón de Transformers, decidir si luchas como Autobot o Decepticon y levantar una base con defensas contundentes. Hay lanzamisiles, torres de choque y turretas láser, todo orientado a frenar incursiones rivales y preparar tu asalto. Su estructura recuerda a otros juegos de su género en móviles, pero con la capa extra de estar en una franquicia mítica. Eso se traduce en misiones y eventos que apelan a la nostalgia, y en una progresión que combina desbloqueo de personajes y mejora de estructuras para alcanzar mejores ligas. Este juego se sitúa en la orilla opuesta: acción directa, combates uno contra uno y controles pensados para que cualquier persona pueda subirse al ring de metal en segundos. El panel de control ofrece movimientos específicos, jabs y golpes al estilo boxeo pero en clave mecha, con impactos sólidos que se sienten bien en pantalla táctil. La progresión consiste en mejorar a tu robot paso a paso: desbloqueas armas, refuerzas defensa y ajustas el conjunto para tumbar a los enemigos más duros. Además, introduce un giro llamativo: también peleas contra dinosaurios gigantes, de modo que no todo son duelos entre robots, sino que hay jefes que exigen leer patrones y variar la estrategia. La idea es clara: partidas cortas, impacto visual y combates que suben de dificultad a medida que avanzas. Si te gusta la táctica ligera y progresar a tu ritmo, aquí hay horas de entretenimiento sin complicaciones. Muchos recordamos War Robots como el gran referente de PvP mecha en móvil. El testimonio que hemos visto habla de que era “el mejor juego PvP de robots mecha” y plantea la pregunta de si a día de hoy sigue siendo el rey. Aunque el contenido analizado no aporta fichas detalladas actuales, el eco que deja es que su fórmula competitiva marcó época, y que durante el tiempo fuera del género surgieron proyectos parecidos, incluso uno de corte ruso que parecía más avanzado cuando aún estaba en desarrollo. Con esa información, la respuesta honesta es que el trono del PvP se discute, y conviene probar alternativas según lo que busques: si quieres una experiencia de arena con builds complejos, War Robots sigue siendo un punto de referencia; si prefieres partidas más directas, Mechangelion ofrece duelos rápidos con progresión clara; y si buscas el plus creativo de diseñar y competir, Robot Crash Fight suma ingredientes interesantes. Aquí la gracia está en ser ingeniero y piloto a la vez. Empiezas por diseñar y operar robots teledirigidos, blindados y armados, para combatir en un torneo de eliminación en arenas. El bucle jugable combina construir, equipar y chocar contra rivales en 3D con animaciones vistosas. El arsenal es variado y un punto gamberro: desde sierras y lanzallamas hasta martillos, descargas eléctricas y imanes con usos tácticos. La personalización es la clave: cada pieza que añades cambia cómo rindes y cómo respondes a ciertas amenazas del rival. Visualmente cumple con creces en móviles, con gráficos 3D de calidad y físicas que dan buen feedback cuando impactas o te impactan. Si te atrae la idea de iterar sobre tu máquina y encontrar combinaciones ganadoras, tiene ese “una más y lo dejo” muy propio de las arenas. Aunque no son juegos de móvil, varias webs que posicionan bien incluyen títulos clave de mechas en consola y PC. No los vas a jugar en Android, pero sirven para entender en qué se inspiran muchos proyectos móviles y qué hace grande a este subgénero. La saga de FromSoftware regresó tras una larga pausa con una entrega centrada en robots enormes y totalmente configurables. No es un “soulslike”, sino un Armored Core puro, de acción exigente y ajustes finos de piezas, armas y propulsores. Su prestigio reside en el pulso del combate y en lo que se llega a exprimir la personalización. Una aproximación muy distinta: aquí se mezcla rol narrativo de ciencia ficción con estrategia a dos dimensiones y un arte espectacular marca de Vanillaware. Su fuerza no está en el choque directo de metal, sino en cómo cuenta doce historias entrelazadas y las lleva a escenarios de batallas tácticas. Estrategia en tiempo real ambientada en una Europa alternativa post Primera Guerra Mundial, inspirada en el trabajo de Jakub Różalski. Tres facciones (con guiños a Rusia, Polonia e Inglaterra) y mechas estilo steampunk que cambian cómo se mueven y pelean tus escuadras. Tiene multijugador para medirse con otras personas. Acción y estrategia a los mandos de un escuadrón de cinco mechas, con escenarios destruibles y cooperativo online para cinco jugadores. La propuesta mezcla disparos, gestión de equipo y misiones en cadena, apostando por coordinar roles dentro del grupo. Simulación de combate en el universo BattleTech. Encarnas a un piloto mercenario, aceptas contratos, mejoras tus mechs y sobrevives en un mapa galáctico con conflictos constantes. Es una experiencia más pausada, de planificación y ejecución de operaciones. Secuela del juego de 2019 con robots configurables, exploración en mundos abiertos y combates intensos tanto en tierra como en el aire. Abundan los jefes colosales y un sistema de progreso basado en piezas y habilidades que invitan a seguir farmeando. Spin-off con foco en la narrativa y el combate dentro del universo Gundam, desarrollado por B.B. Studio y publicado por Bandai Namco. Se lanzó en PlayStation y combina misiones con momentos de historia que expanden su mundo. Si buscas un duelo de reflejos puro y duro, lo tuyo es una experiencia tipo Mechangelion: movimientos claros, lectura del rival y ajustes de daño/defensa que notas de inmediato. Recomendable si quieres partidas cortas con sensación de progreso rápida. Para un enfoque más táctico con capas de gestión, la propuesta de Transformers: Earth Wars brilla: crear una base, decidir la composición de tu equipo y planificar ataques es tan importante como ejecutar. Es una opción ideal si prefieres pensar a medio plazo y ver crecer tu fortaleza. ¿Te motiva construir prototipos y experimentar? Entonces encaja Robot Crash Fight, porque el metajuego de diseñar, probar y volver a ajustar engancha. Cada arma tiene usos situacionales (esa sierra que destroza cerca, ese imán que desbarata la jugada rival), y encontrar la sinergia marca la diferencia. Si lo que persigues es una arena PvP de largo recorrido, el recuerdo de War Robots sigue pesando. Con la información analizada, no podemos dar una ficha minuciosa actualizada, pero sí queda claro que su ADN competitivo sigue siendo referencia al hablar de mechas en móvil. Define primero tu estilo: si priorizas acción inmediata y progresión simple, Mechangelion te lo pone fácil; si te tira la estrategia con licencias míticas, Earth Wars te permite vivir el conflicto Autobot/Decepticon; si quieres construir y competir con creatividad, Robot Crash Fight te va a entretener con su arsenal loco. Valora también el tiempo que le dedicarás: los juegos de estrategia recompensan la constancia y la planificación diaria, mientras que los duelos 1v1 aceptan mejor sesiones cortas. Y en arenas PvP, cuanto más juegas, más entiendes el metajuego, así que piensa en tu curva de aprendizaje. Respondiendo a la duda recurrente de si War Robots sigue siendo el mejor PvP: con los datos revisados, no hay un “único rey” incontestable, sino estilos que encajan con gustos distintos. Si añoras el feeling de arena, dale una oportunidad a War Robots y mide sensaciones; si te apetece algo más directo, Mechangelion cumple; si quieres construcción y customización extrema, Robot Crash Fight puede ser tu campo de pruebas. Y ojo con las expectativas: los referentes de PC y consola nos enseñan que el género es amplio y profundo, pero en Android brilla cuando se centra en sesiones ágiles y progresión clara. Si un título te anima a volver cada día con una meta concreta, vas por buen camino. Con este panorama, tienes dónde elegir: estrategia con Transformers, duelos de mechas a golpe limpio y torneos de máquinas diseñadas a medida, todo en tu móvil. Elige según tu tiempo y tu estilo, prueba lo que más te llame y deja que el acero, las chispas y las builds hagan el resto.

News & UpdatesAdvertiseFAQPartner Aktuelle UploadsDie Top 100 TageszeitungenWochenzeitungenMonatszeitungenMagazine Sammlungen WesternBiographieEbook KWErzählungenFantasyHistorikHorrorHumorKinder/JugendKrimiLiebesromanePsychothrillerRomanScience-FictionThriller- Mystery-Thriller Erotik German-ComicsEnglish Comics- ErotikHentai/Manga GesundheitRezeptePolitik/GeschichteRatgeberReiseführerSonstige Met-ArtPenthousePlayboySonstige RatgeberNY-BestsellerNovel Hörbücher Android AppzMacWindowsWallpapers Kurzbeschreibung:This book takes young readers on an exciting adventure with a child named Kai. One day, Kai wakes up trapped inside a giant robot. He cant talk to anyone outside, and the only way to communicate is through the robot. Inside the robot, Kai finds many books and documents written in a strange language its the robots language, which is Python. Kai realizes he needs to learn this language to control the robot and talk to the outside world. In each chapter in this book, we will join Kai on a new adventure to learn something that helps us control the robot better and communicate with the real world. This fun and interactive book is designed to introduce young minds to the basics of programming while encouraging creativity and problem-solving skills.In the introductory chapters, readers discover Python as a friendly and accessible programming language. The book guides them through setting up their programming environment and crafting their initial lines of code, laying the foundation for an exciting coding adventure. As the exploration unfolds, it delves into fundamental programming concepts essential for any budding coder. From variables and data types to loops and conditionals, these building blocks empower readers to create their programs, fostering a solid understanding of the core principles of coding. It seamlessly integrates these concepts with previously learned fundamentals, providing a comprehensive view of Pythons capabilities. Fueling creativity, it inspires readers to unleash their imagination through engaging projects. From crafting games to developing useful applications, young coders learn to apply their programming skills in innovative ways, transforming abstract coding concepts into real and interactive projects.With a focus on accessibility, engagement, and real-world application, this book paves the way for the next generation of Python enthusiasts.What you will learn:Understand Python programming fundamentals, including syntax, variables, data types, loops, conditionals, lists, functions, and handling files.Learn to break down complex problems into smaller, manageable tasks and apply coding concepts to find creative solutions.How to create their interactive coding projects using Python.Understand strategies for debugging and troubleshooting common programming problems, which are essential skills for any programmerWho this book is for:This book caters primarily for high school students and individuals keen on delving into programming with minimal or zero coding background. Its structured to be both accessible and captivating for young readers, immersing them in the realm of coding through entertaining and interactive journeys. Moreover, it extends its reach to educators and coding enthusiasts alike. Erweiterte Suche 01. Raidrush *HOT*02. Nydus.org03. archivx.to04. Byte.to *HOT*05. GLOAD.TO06. SZENE.LiNK07. Stream DDL Suche08. startseite.to09. ddl.raidrush.org10. Google11. hoerbuch.us12. WarezLoad13. OneDDL14. Dein Link15. Dein Link Partner werdenAlle Partner Startseite | FAQ zum kostenlosen Download auf ebook-hell.to | Top100 | Disclaimer

November 09, 2021 04:12 ET | Source: NVIDIA NVIDIA SANTA CLARA, Calif., Nov. 09, 2021 (GLOBE NEWSWIRE) -- GTC—NVIDIA today announced NVIDIA Omniverse Avatar, a technology platform for generating interactive AI avatars. Omniverse Avatar connects the company’s technologies in speech AI, computer vision, natural language understanding, recommendation engines and simulation technologies. Avatars created in the platform are interactive characters with ray-traced 3D graphics that can see, speak, converse on a wide range of subjects, and understand naturally spoken intent. Omniverse Avatar opens the door to the creation of AI assistants that are easily customizable for virtually any industry. These could help with the billions of daily customer service interactions — restaurant orders, banking transactions, making personal appointments and reservations, and more — leading to greater business opportunities and improved customer satisfaction. “The dawn of intelligent virtual assistants has arrived,” said Jensen Huang, founder and CEO of NVIDIA. “Omniverse Avatar combines NVIDIA’s foundational graphics, simulation and AI technologies to make some of the most complex real-time applications ever created. The use cases of collaborative robots and virtual assistants are incredible and far reaching.” Omniverse Avatar is part of NVIDIA Omniverse™, a virtual world simulation and collaboration platform for 3D workflows currently in open beta with over 70,000 users. In his keynote address at NVIDIA GTC, Huang shared various examples of Omniverse Avatar: Project Tokkio for customer support, NVIDIA DRIVE Concierge for always-on, intelligent services in vehicles, and Project Maxine for video conferencing. In the first demonstration of Project Tokkio, Huang showed colleagues engaging in a real-time conversation with an avatar crafted as a toy replica of himself — conversing on such topics as biology and climate science. In a second Project Tokkio demo, he highlighted a customer-service avatar in a restaurant kiosk, able to see, converse with and understand two customers as they ordered veggie burgers, fries and drinks. The demonstrations were powered by NVIDIA AI software and Megatron 530B, which is currently the world’s largest customizable language model. In a demo of the DRIVE Concierge AI platform, a digital assistant on the center dashboard screen helps a driver select the best driving mode to reach his destination on time, and then follows his request to set a reminder once the car’s range drops below 100 miles. Separately, Huang showed Project Maxine’s ability to add state-of-the-art video and audio features to virtual collaboration and content creation applications. An English-language speaker is shown on a video call in a noisy cafe, but can be heard clearly without background noise. As she speaks, her words are both transcribed and translated in real time into German, French and Spanish with her same voice and intonation. Omniverse Avatar Key ElementsOmniverse Avatar uses elements from speech AI, computer vision, natural language understanding, recommendation engines, facial animation, and graphics delivered through the following technologies: These technologies are composed into an application and processed in real time using the NVIDIA Unified Compute Framework. Packaged as scalable, customizable microservices, the skills can be securely deployed, managed and orchestrated across multiple locations by NVIDIA Fleet Command™. Learn more about Omniverse Avatar. Register for free to learn more about NVIDIA Omniverse during NVIDIA GTC, taking place online through Nov. 11. Watch Huang’s GTC keynote address streaming on Nov. 9 and in replay. About NVIDIANVIDIA’s (NASDAQ: NVDA) invention of the GPU in 1999 sparked the growth of the PC gaming market and has redefined modern computer graphics, high performance computing, and artificial intelligence. The company’s pioneering work in accelerated computing and AI is reshaping trillion-dollar industries, such as transportation, healthcare and manufacturing, and fueling the growth of many others. More information at https://nvidianews.nvidia.com/. For further information, contact:Kristin UchiyamaSenior PR ManagerNVIDIA Corporation+1-408-313-0448kuchiyama@nvidia.com Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, and features of NVIDIA Omniverse Avatar, Project Tokkio, DRIVE Concierge, Project Maxine, NVIDIA Riva, Megatron 530B, NVIDIA Merlin, NVIDIA Metropolis, NVIDIA Video2Face and Audio2Face, the NVIDIA Unified Compute Framework and NVIDIA Fleet Command; Omniverse Avatar opening the door to the creation of AI assistants that are easily customizable for virtually any industry; the help of AI assistants leading to greater business opportunities and improved customer satisfaction; and the use cases of collaborative robots and virtual assistants are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. © 2021 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, Audio2Face, Maxine, NGC, NVIDIA DRIVE, NVIDIA Fleet Command, NVIDIA Merlin and NVIDIA Omniverse are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. All other trademarks and copyrights are the property of their respective owners. Features, pricing, availability, and specifications are subject to change without notice. A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/35c4d67a-361e-4693-b500-289ff3c9dbc0 News Summary: The Nemotron 3 family of open models — in Nano, Super and Ultra sizes — introduces the most efficient family of open models with leading accuracy for building agentic AI... SANTA CLARA, Calif., Nov. 20, 2025 (GLOBE NEWSWIRE) -- NVIDIA will present at the following event for the financial community: UBS Global Technology and AI ConferenceTuesday, Dec. 2, 6:35 a.m....

Searching for your content... In-Language News Contact Us 888-776-0942 from 8 AM - 10 PM ET Jul 14, 2021, 23:11 ET Share this article BEIJING, July 14, 2021 /PRNewswire/ -- China International Capital Corporation Limited (CICC, 3908.HK, 601995.SH) held the Investment and Financing Theme Forum at the 2021 World Artificial Intelligence Conference in Shanghai, during which it published a research report, Achieving Carbon Neutrality amid Thriving AI. CICC Research's Technology Group has joined together with multiple sector research teams to complete this in-depth analysis covering potential scenarios for AI-enabled carbon emission reduction. The report demonstrates the practical effects and the evolutionary trends of AI in terms of improving efficiency, saving energy, as well as reducing emissions consumption. Peng Hu, Chief Analyst of CICC's Technology Hardware Group, delivered a keynote speech to the conference, analyzing how AI can help achieve carbon neutrality, as well as the investment opportunities in the technology industry, for example, AI-enabled cities, AI-enabled vehicles, AI-enabled smart manufacturing, and AI-enabled power. Carbon neutrality, an important application for AI After the 21st United Nations Climate Change Conference, the achievement of carbon neutrality has become a global goal. As of the end of 2020, 44 countries and regions around the world made commitments on carbon neutrality. China also announced that it would achieve peak carbon levels by 2030 and carbon neutrality by 2060. CICC believes that the key to achieving carbon neutrality lies in the reduction of carbon dioxide emissions per unit of GDP, which puts pressure on areas such as energy, transportation, manufacturing and urban construction planning in the context of China's rapid economic growth. AI is expected to promote efficiency and consumption reduction in a number of different areas and help achieve the goal of carbon neutrality. Peng explains that AI can help in three ways: prediction, monitoring and optimization. AI helps carbon neutrality in four major areas In Peng's view, AI could help achieve the goal of carbon neutrality in the four fields, namely cities, smart manufacturing, vehicles and power. Investment opportunities in the science and technology industries against the backdrop of carbon neutrality ESG investing has become a global trend. Peng believes that the ESG investment philosophy will prompt listed companies to pay more attention to the control of carbon emissions. Over the long term, companies with higher ESG level will achieve better operation results and more sustainable returns, which creates new investment opportunities. Peng points out that AI should also be filled with "humanistic care", which is not only a technical term that means increase efficiency and profits, but also plays a greater role in improving the living environment, creating social welfare and enhancing human well-being. Peng suggests to focus the investment opportunities in the following 10 areas amid AI-enabled carbon neutrality: 1) smart power grids; 2) drones for civilian use; 3) mobile robots; 4) industrial internet platforms; 5) machine vision; 6) smart cities; 7) cloud computing; 8) AI chips; 9) intelligent driving; and 10) sensors. According to the CICC's forecast, the increase of the market size of these 10 areas is around RMB 2 trillion in China over the next decade (2021–2030). To read the full research report, click here: https://en.cicc.com/api/upload/uploadService/dowloadEx?fileId=24884&tenantId=123890 China International Capital Corporation Limited (CICC): China International Capital Corporation Limited (CICC, 03908.HK，601995.SH) is a top tier investment bank, founded in China in 1995, providing first-class financial services to corporates, institutions and individuals worldwide. As the first international joint-venture investment bank in China, CICC plays a unique role to support China's economic reforms and liberalization through providing comprehensive one-stop domestic, overseas, and cross-border financial services including investment banking, equities, FICC, asset management, private equity investment, wealth management and research. Headquartered in Beijing, CICC has over 200 branches in Mainland China and offices in Hong Kong, Singapore, New York, London, San Francisco, Frankfurt and Tokyo. For more information about CICC, please visit www.cicc.com SOURCE China International Capital Corporation Limited Do not sell or share my personal information:

Searching for your content... In-Language News Contact Us 888-776-0942 from 8 AM - 10 PM ET Mar 15, 2022, 11:30 ET Share this article DUBLIN, March 15, 2022 /PRNewswire/ -- The "Automation and Robotics Market in Industrial, Enterprise, Military, and Consumer Segments by Type, Components, Hardware, Software, and Services 2022 - 2027" report has been added to ResearchAndMarkets.com's offering. This report evaluates the global and regional robotics marketplace including the technologies, companies, and solutions for robots in the industrial, enterprise, military, and consumer segments. The report includes detailed forecasts for robotics by robot type, components, capabilities, solutions, and connectivity for 2022 to 2027.Select Report Findings: With the substantial amount of capital behind global industrial automation, the industrial robotics sector will continue a healthy growth trajectory, which is supported by many qualitative and quantitative benefits including cost reduction, improved quality, increased production, and improved workplace health and safety.In the wake of the pandemic, we see a major push for further automation and robotics, especially within the United States service sector. This is because many businesses see repetitive tasks as performed with great safety, less expense, and reduced probability for service disruption with robotics rather than reliance upon human workers.Robotics is increasingly used to improve enterprise, industrial, and military automation. In addition, robots are finding their way into more consumer use cases as the general public's concerns fade and acceptance grows in terms of benefits versus risks. While many consumer applications continue to be largely lifestyle-oriented, enterprise, industrial, and military organizations utilize both land-based and aerial robots that are used for various repetitive, tedious, and/or dangerous tasks. Adoption and usage are anticipated to rapidly increase with improvements to artificial intelligence, robotic form factors, and fitness for use, cloud computing, and related business models, such as robotics as a service.The global robotics market is broadly segmented into enterprise, industrial, military, and consumer robotics. Major market segments that cross-over industries include Healthcare bots, Unmanned Aerial Vehicles, and autonomous vehicles. Enterprise Robotics includes the use of robots for both business-to-business and business-to-consumer services and support. Functions include internal business operations and processes, delivery of goods and services, research, analytics, and other business-specific applications.The next decade will witness substantial influence of AI upon robotics. The next generation of robotics will include many pre-integrated AI technologies such as machine vision, voice and speech recognition, tactile sensors, and gesture controls. AI has enabled consumer robots to learn while performing a variety of tasks including cleaning, controlling home appliances, reading, performing butler services, and many more. It is anticipated that further improvement in AI and related technologies such as cognitive computing and sensor fusion, will enable consumer robots to take on increasingly more difficult tasks.The degree to which AI capabilities in robotics optimize operational efficiency and effectiveness will largely depend on the contextual capabilities of connected devices. This means that the relationship of IoT to AI and robotics will be much intertwined. AI helps robots learn and become more effective, but both technologies depend on IoT networks for sharing information among devices and applications.Longer-term, the publisher sees many robotics and automation solutions involving multiple AI types as well as integration across other key areas such as the Internet of Things (IoT) and data analytics. The combination of AI and the IoT has the potential to dramatically accelerate the benefits of robotics for consumer, enterprise, industrial, and government market segments.Leading industry verticals are beginning to see improved operational efficiency through the intelligent combination of AI and robotics. The long-term prospect for these technologies is that they will become embedded in many different other technologies and provide autonomous decision-making on behalf of humans, both directly, and indirectly through many processes, products, and services.The military robotics market is an important segment from both an R&D perspective (e.g. many robotics innovations are funded by government/military projects) as well as cross-over into business and consumer markets such as the public safety arena. The consumer robotics sector is in its infantile stage but is anticipated to exceed all other sectors in terms of scale, variety, and impact in the long run.We see substantial overall industry growth across a wide range of robot types that engage in diverse tasks such as home cleaning, personalized healthcare service, home security, autonomous cars, robotic entertainment, and personal care services, managing daily schedules, and various assistive tasks. A few key factors such as the ageing population, personalization services trends, and robot mobility will drive growth in this industry segment.Robotics in business will accelerate as less expensive hardware and improvements in AI lead to improved cost structures and increased integration with enterprise software systems respectively. The massive amount of data generated by robotics will create opportunities for data analytics and AI-enabled decision support systems. Enterprise users will capitalize upon new and enhanced robotics capabilities to enable new use cases and improved workflow. Many business processes will change as the enterprise becomes savvier about the flexibility of robotics uninhibited by bandwidth constraints. Key Topics Covered: 1.0 Executive Summary2.0 Robotics Market Overview 3.0 Robotics and Automation Technology Trends 4.0 Robotics and Automation in Business Transformation 5.0 Robotics Companies and Solutions 6.0 Global Robotics Forecast 2022 - 20277.0 Industrial Robotics Market 2022 - 20278.0 Consumer Robotics Market 2022 - 20279.0 Enterprise Robotics Market 2022 - 202710.0 Military and Government Robotics Market 2022 - 202711.0 Conclusions and Recommendations For more information about this report visit https://www.researchandmarkets.com/r/idfunf Media Contact: Research and Markets Laura Wood, Senior Manager [email protected] For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900 U.S. Fax: 646-607-1904 Fax (outside U.S.): +353-1-481-1716 SOURCE Research and Markets Do not sell or share my personal information:

Nvidia's new Cosmos model is another sign that devices and machines are getting better at understanding their environments. Meta's Ray-Ban glasses are getting popular. Tech gadgets -- whether they be phones, robots or autonomous vehicles -- are getting better at understanding the world around us, thanks to AI. That message rang loud and clear throughout 2024 and became even louder at CES 2025, where chipmaker Nvidia unveiled a new AI model (a CES award winner) for understanding the physical world and a family of large language models for powering future AI agents. Nvidia CEO Jensen Huang is positioning these world-foundational models as being ideal for robots and autonomous vehicles. There's another class of devices that could benefit from better real-world understanding: smart glasses. Tech-enabled eyewear like Meta's Ray-Bans is quickly becoming the hot new AI gadget, with shipments of Meta's spectacles crossing the 1 million mark in November according to Counterpoint Research. Such devices seem like the ideal vessel for AI agents, or AI helpers that can understand the world around you using cameras and processing speech and visual input to help you get things done rather than just answering questions. Huang didn't say whether Nvidia-powered smart glasses are on the horizon. He did explain how the company's new model would power future smart glasses if partners were to adopt the technology for that purpose. "The use of AI as it gets connected to wearables and virtual presence technology like glasses, all of that is super exciting," Huang said in response to a question about whether its models would work on smart glasses during a press Q&A at CES. Read more: Smart Glasses Are Going to Work This Time, Google's Android President Tells CNET Huang pointed to cloud processing as an option, which would mean queries that use Nvidia's Cosmos model could be handled in the cloud rather than on the device itself. Compact devices like smartphones use this method often to lighten the processing load when running demanding AI models. If a device maker wanted to create glasses that could leverage Nvidia's AI models on-device rather than relying on the cloud, Huang said Cosmos would distill its knowledge into a smaller model that's less generalized and optimized for specific tasks. Nvidia's new Cosmos model is being touted as a platform to gather data about the physical world to train models for robots and self-driving cars -- similar to the way a large language model would learn how to generate text responses after being trained on written media. "The ChatGPT moment for robotics is coming," Huang said in a press release. Nvidia also announced a set of new AI models built with Meta's Llama technology called Llama Nemotron, which is designed to accelerate the development of AI agents. It's interesting to think about how these AI tools and models could potentially be applied to smart glasses too. A recent Nvidia patent filing fueled speculation about upcoming smart glasses, although the chipmaker hasn't made any announcements about future products in that space. Nvidia's new models and Huang's comments come as Google, Samsung and Qualcomm announced last month that they're building a new mixed-reality platform for smart glasses and headsets called Android XR, hinting that smart glasses could soon become more prominent. Several new types of smart glasses were also on display at CES 2025, such as the RayNeo X3 Pro and Halliday smart glasses. The International Data Corporation also predicted in September that shipments of smart glasses would grow by 73.1% in 2024. Nvidia's moves are another space to watch for too. For more from CES, check out the best TVs, the best laptops and the most impressive concepts we've seen.

A "neural codec language model", VALL-E uses discrete codes derived from an off-the-shelf neural audio codec model to synthesize high-quality personalised speech with only a 3-second recording of an unseen speaker. The AI is trained with 60,000 hours of English speech with over 7,000 unique speakers. All this data is taken from Libri-Light, the Meta-owned audio library that collects spoken English audio. It can also imitate the speaker's emotional tone and acoustic environment. "Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity," say Microsoft researchers in their paper. The three-second voice input needs to match with some other samples in the training data provided to have a better result. This is why VALL-E should be more diverse in the future. The training data will be scaled up to improve the performances of prosody, speaking style, and speaker similarity perspectives, Microsoft says. READ MORE: AI offers solutions, but needs to be more transparent: experts How can we benefit from VALL-E? For now VALL-E can only convert text into speech in the chosen voice. It can’t create new content. Its creators are hopeful that VALL-E can provide various benefits in terms of speech editing and audio content creation. The example of Stephen Hawking using a text-to-speech generator to continue his studies while suffering from classical motor neuron disease (ALS) has shown the world one of the highest benefits one could get from this technology. VALL-E can be used for simultaneous translations, or to create the voice of our loved ones who had passed away. Creating audiobooks would be a lot easier and faster with VALL-E. One can create a voice for any written peace or text message in a short time. For all these uses and more, we need to wait for Microsoft to open VALL-E to public use. Microsoft has not said yet when the new AI will be available for public consumption. READ MORE:Can an invention enabled by artificial intelligence be patented? VALL-E might bring risks While the question of how to use AI technologies safely and ethically is being asked more often than ever these days, people express their ethical concerns over newly launched systems like Chat GPT, Lensa AI, or VALL-E. Chat GPT, a new chatbot AIthat can process natural language tasks such as text generation and language translation started debates on students committing plagiarism by using this AI for their homework. At the same time, Lensa AI, an app that uses algorithms to generate ordinary photos into artistic renderings led to ethical questions on artistic production made by using other artists' works. Many argue that it cannot replace human artists who make digital art. VALL-E, likewise, has potential risks of misuse that can criminalise the users, such as spoofing voice identification or impersonating a specific speaker. Impersonating people's voices without their consent might fuel mischief and deception which could then lead to social harm. Similar to Lensa's risk of replacing real artists, VALL-E also leads to concerns over ethics of art. In a case when music production companies make the AI sing new songs without the consent of the voice-owner singer, it might not be that much fun to use it. READ MORE: Robots can be racist and sexist, new study warns Microsoft's response to concerns Microsoft says it is aware of these concerns and possible risks that robots might bring on. It had apologised in the past for its chatbot Tay's offensive tweets. The researchers who created VALL-E stated in their paper that they are likely to build a measuring mechanism that can prevent such risks. "Since VALL-E could synthesize speech that maintains speaker identity, it may carry potential risks in misuse of the model, such as spoofing voice identification or impersonating a specific speaker," they said in the paper. "To mitigate such risks, it is possible to build a detection model to discriminate whether an audio clip was synthesized by VALL-E. We will also put Microsoft AI Principles into practice when further developing the models." they added. The company will see if it could build such a detection model and if so, how much of these risks will be mitigated by it, only when the project is open to public use, they said. READ MORE:AI offers solutions, but needs to be more transparent: experts 00:00 00:00

September 27, 2022 12:15 ET | Source: Market Research Future Market Research Future New York, US, Sept. 27, 2022 (GLOBE NEWSWIRE) -- According to a comprehensive research report by Market Research Future (MRFR), “Speech Recognition Market, By Type, Technology, Verticals - Forecast Till 2030”, to garner USD 48.8 billion by 2030, registering approximately 21.30% CAGR during the review period (2022-2030). Speech Recognition Market Overview The speech recognition market witness’s rapid revenue growth due to the increasing use in the education sectors globally. Besides, the rising demand for accurate and easy-to-use speech recognition APIs suitable for multiple fields and languages fosters market growth. The speech-to-text industry that perceives brisk digitization impact market shares positively. Also, other industries moving toward digital transformation boost the speech recognition market size. Players active in the speech recognition market are, Get Free Sample PDF Brochure: https://www.marketresearchfuture.com/sample_request/1815 Users of speech recognition technologies are demanding much more than ever. New and changing industrial safety regulations are forcing industries to change workflows and adapt to the changes. Resultantly, the requirements of speech recognition solutions have expanded beyond simple text transcription, adding advanced capabilities like punctuation, speaker notes, global language packs, organizational reforms, and new vocabulary with frequent builds. With the growing adoption of new voice-enabled learning tools that can address equity and bias issues, the speech recognition market is likely to gain significant traction in the next few years. Over the past several years, the introduction of breakthrough technologies and digital transformation in various industries has been growing exponentially. This, alongside the resultant scale-up in the speech-to-text industry, is promoting the need to meet these new technological requirements. Also, new developments of self-supervised learning solutions that enable speech recognition engines to learn from unstructured data across the web are increasingly coming forward. Self-supervised learning allows access to a large range of unlabeled data, social media, etc., offers a wider variety of voices, and eliminates the need for human supervision. Industry Trends There are a number of researches to drive advances in speech recognition technology to equally diversify and understand accents & dialects clearly. Besides, the rising use of new voice-enabled learning tools demonstrating the capability to address equity and bias creates vast opportunities. The growing adoption of speech recognition technology in education drives the speech recognition market growth. Additionally, significant investments in developing high-performance and efficient speech recognition boost the market size. Large upgrades in major offerings have enabled educational applications that are accelerating demand for more joyous and frictionless voice experiences, accelerating the speech recognition market value. These advanced educational tools have boosted the interest of developers, educators, and researchers, further widening the access to educational resources and minimizing the risks of implicit biases. Report Scope: Browse In-depth Market Research Report (100 Pages) on Speech Recognition Market: https://www.marketresearchfuture.com/reports/speech-recognition-market-1815 Speech Recognition Market Regional Analysis North America leads the global market for speech recognition technology. Growing implementations of speech recognition applications in cell phones escalate the market value. Besides, the expanding utilization of this technology for obtaining customer consent/ acknowledgment over speech/voice in mobile banking and buyers & IoT gadgets boost the speech recognition market size. Rapid developments and rising applications of this technology substantiate market revenues. Moreover, the large presence of major technology providers and high spending on automotive infotainment systems increase the market sales of speech recognition. Europe is another profitable market for speech recognition, headed by the rising interest in discourse and voice acknowledgment. Additionally, voice recognition tech observing significant advances and expanded applications in buyer gadgets and retail areas drives the speech recognition market demand. The vast automotive sector in the region creates substantial market demand, increasingly using patterns of associated devices in robots. APAC is an emerging market for speech recognition systems. The growing number of developments and use of voice-empowered gadgets in the auto and medical businesses push the market growth. Furthermore, augmenting demand for standalone products of language recognition, speech-to-text, machine translation, and transliteration provides significant market opportunities. China, Japan, and Singapore are major speech recognition markets supporting the growth of the regional market. Speech Recognition Market Segments This speech recognition market is segmented into vehicle types, technologies, verticals, and regions. The type segment is bifurcated into speaker-dependent and speaker-independent. The technology segment is bifurcated into AI-based and non-AI-based. The vertical segment is bifurcated into military, automotive, healthcare, and others. The region segment is bifurcated into the Americas, Asia-Pacific, Middle East & Africa, Europe, and rest-of-the-world. Ask for Discount: https://www.marketresearchfuture.com/sample_request/1815 Speech Recognition Market Competitive Analysis The highly competitive speech recognition market appears fragmented due to the presence of several well-established players. To gain a competitive advantage, industry players adopt strategic approaches such as collaborations, mergers & acquisitions, expansions, and product/ technology launches. Speech recognition technology enables learning and plays experiences. Therefore, speech recognition market players are integrating this technology with AI to develop efficient literacy solutions to quickly and accurately assess reading and language skills. This gives teaching professionals a seamless, scalable, and more accurate approach to assessing the language reading & learning progress of students. Further, market players make substantial investments in driving their R&D activities and expansion plans. For instance, on Sep.21, 2022, Nvidia Corporation, an American multinational technology company, unveiled new languages and other upgrades to its Riva speech AI platform for enterprise services. Last year, Nvidia launched the Riva Custom Voice neural-based text-to-speech service that can create lifelike human voices. Talk to Expert: https://www.marketresearchfuture.com/check-discount/1815 Nvidia Riva now features seven language models and tools for distinguishing speakers in conversations and more customization options. Nvidia's Riva speech AI software development kit (SDK) features speech recognition and text-to-speech functions required in enterprises to augment the existing automatic speech recognition and domain-specific customization. Related Reports: Global Speech Analytics Market, by Type, by Deployment Type, by End-User, by Organization Size - Forecast 2030 Far-Field Speech and Voice Recognition Market Research Report: By Component, Microphone Solution, Application - Forecast till 2030 Voice Recognition System Market for Automotive Information Report by Vehicle Type, Technology, Application and by Regions - Global Forecast To 2030 VoLTE Technology Market Research Report, By Device, Technology - Forecast till 2030 About Market Research Future: Market Research Future (MRFR) is a global market research company that takes pride in its services, offering a complete and accurate analysis regarding diverse markets and consumers worldwide. Market Research Future has the distinguished objective of providing the optimal quality research and granular research to clients. Our market research studies by products, services, technologies, applications, end users, and market players for global, regional, and country level market segments, enable our clients to see more, know more, and do more, which help answer your most important questions. Follow Us: LinkedIn | Twitter

Author of the article: You can save this article by registering for free here. Or sign-in if you have an account. KYIV, Ukraine (AP) — Drone advances in Ukraine have accelerated a long-anticipated technology trend that could soon bring the world’s first fully autonomous fighting robots to the battlefield, inaugurating a new age of warfare. Enjoy the latest local, national and international news. Enjoy the latest local, national and international news. Create an account or sign in to continue with your reading experience. Create an account or sign in to continue with your reading experience. The longer the war lasts, the more likely it becomes that drones will be used to identify, select and attack targets without help from humans, according to military analysts, combatants and artificial intelligence researchers. That would mark a revolution in military technology as profound as the introduction of the machine gun. Ukraine already has semi-autonomous attack drones and counter-drone weapons endowed with AI. Russia also claims to possess AI weaponry, though the claims are unproven. But there are no confirmed instances of a nation putting into combat robots that have killed entirely on their own. Experts say it may be only a matter of time before either Russia or Ukraine, or both, deploy them. “Many states are developing this technology,” said Zachary Kallenborn, a George Mason University weapons innovation analyst. “Clearly, it’s not all that difficult.” The sense of inevitability extends to activists, who have tried for years to ban killer drones but now believe they must settle for trying to restrict the weapons’ offensive use. Ukraine’s digital transformation minister, Mykhailo Fedorov, agrees that fully autonomous killer drones are “a logical and inevitable next step” in weapons development. He said Ukraine has been doing “a lot of R&D in this direction.” “I think that the potential for this is great in the next six months,” Fedorov told The Associated Press in a recent interview. Ukrainian Lt. Col. Yaroslav Honchar, co-founder of the combat drone innovation nonprofit Aerorozvidka, said in a recent interview near the front that human war fighters simply cannot process information and make decisions as quickly as machines. Ukrainian military leaders currently prohibit the use of fully independent lethal weapons, although that could change, he said. “We have not crossed this line yet — and I say ‘yet’ because I don’t know what will happen in the future.” said Honchar, whose group has spearheaded drone innovation in Ukraine, converting cheap commercial drones into lethal weapons. Russia could obtain autonomous AI from Iran or elsewhere. The long-range Shahed-136 exploding drones supplied by Iran have crippled Ukrainian power plants and terrorized civilians but are not especially smart. Iran has other drones in its evolving arsenal that it says feature AI. Without a great deal of trouble, Ukraine could make its semi-autonomous weaponized drones fully independent in order to better survive battlefield jamming, their Western manufacturers say. Those drones include the U.S.-made Switchblade 600 and the Polish Warmate, which both currently require a human to choose targets over a live video feed. AI finishes the job. The drones, technically known as “loitering munitions,” can hover for minutes over a target, awaiting a clean shot. “The technology to achieve a fully autonomous mission with Switchblade pretty much exists today,” said Wahid Nawabi, CEO of AeroVironment, its maker. That will require a policy change — to remove the human from the decision-making loop — that he estimates is three years away. Drones can already recognize targets such as armored vehicles using cataloged images. But there is disagreement over whether the technology is reliable enough to ensure that the machines don’t err and take the lives of noncombatants. The AP asked the defense ministries of Ukraine and Russia if they have used autonomous weapons offensively — and whether they would agree not to use them if the other side similarly agreed. Neither responded. If either side were to go on the attack with full AI, it might not even be a first. An inconclusive U.N. report suggested that killer robots debuted in Libya’s internecine conflict in 2020, when Turkish-made Kargu-2 drones in full-automatic mode killed an unspecified number of combatants. A spokesman for STM, the manufacturer, said the report was based on “speculative, unverified” information and “should not be taken seriously.” He told the AP the Kargu-2 cannot attack a target until the operator tells it to do so. Fully autonomous AI is already helping to defend Ukraine. Utah-based Fortem Technologies has supplied the Ukrainian military with drone-hunting systems that combine small radars and unmanned aerial vehicles, both powered by AI. The radars are designed to identify enemy drones, which the UAVs then disable by firing nets at them — all without human assistance. The number of AI-endowed drones keeps growing. Israel has been exporting them for decades. Its radar-killing Harpy can hover over anti-aircraft radar for up to nine hours waiting for them to power up. Other examples include Beijing’s Blowfish-3 unmanned weaponized helicopter. Russia has been working on a nuclear-tipped underwater AI drone called the Poseidon. The Dutch are currently testing a ground robot with a .50-caliber machine gun. Honchar believes Russia, whose attacks on Ukrainian civilians have shown little regard for international law, would have used killer autonomous drones by now if the Kremlin had them. “I don’t think they’d have any scruples,” agreed Adam Bartosiewicz, vice president of WB Group, which makes the Warmate. AI is a priority for Russia. President Vladimir Putin said in 2017 that whoever dominates that technology will rule the world. In a Dec. 21 speech, he expressed confidence in the Russian arms industry’s ability to embed AI in war machines, stressing that “the most effective weapons systems are those that operate quickly and practically in an automatic mode.” Russian officials already claim their Lancet drone can operate with full autonomy. “It’s not going to be easy to know if and when Russia crosses that line,” said Gregory C. Allen, former director of strategy and policy at the Pentagon’s Joint Artificial Intelligence Center. Switching a drone from remote piloting to full autonomy might not be perceptible. To date, drones able to work in both modes have performed better when piloted by a human, Allen said. The technology is not especially complicated, said University of California-Berkeley professor Stuart Russell, a top AI researcher. In the mid-2010s, colleagues he polled agreed that graduate students could, in a single term, produce an autonomous drone “capable of finding and killing an individual, let’s say, inside a building,” he said. An effort to lay international ground rules for military drones has so far been fruitless. Nine years of informal United Nations talks in Geneva made little headway, with major powers including the United States and Russia opposing a ban. The last session, in December, ended with no new round scheduled. Washington policymakers say they won’t agree to a ban because rivals developing drones cannot be trusted to use them ethically. Toby Walsh, an Australian academic who, like Russell, campaigns against killer robots, hopes to achieve a consensus on some limits, including a ban on systems that use facial recognition and other data to identify or attack individuals or categories of people. “If we are not careful, they are going to proliferate much more easily than nuclear weapons,” said Walsh, author of “Machines Behaving Badly.” “If you can get a robot to kill one person, you can get it to kill a thousand.” Scientists also worry about AI weapons being repurposed by terrorists. In one feared scenario, the U.S. military spends hundreds of millions writing code to power killer drones. Then it gets stolen and copied, effectively giving terrorists the same weapon. To date, the Pentagon has neither clearly defined “an AI-enabled autonomous weapon” nor authorized a single such weapon for use by U.S. troops, said Allen, the former Defense Department official. Any proposed system must be approved by the chairman of the Joint Chiefs of Staff and two undersecretaries. That’s not stopping the weapons from being developed across the U.S. Projects are underway at the Defense Advanced Research Projects Agency, military labs, academic institutions and in the private sector. The Pentagon has emphasized using AI to augment human warriors. The Air Force is studying ways to pair pilots with drone wingmen. A booster of the idea, former Deputy Defense Secretary Robert O. Work, said in a report last month that it “would be crazy not to go to an autonomous system” once AI-enabled systems outperform humans — a threshold that he said was crossed in 2015, when computer vision eclipsed that of humans. Humans have already been pushed out in some defensive systems. Israel’s Iron Dome missile shield is authorized to open fire automatically, although it is said to be monitored by a person who can intervene if the system goes after the wrong target. Multiple countries, and every branch of the U.S. military, are developing drones that can attack in deadly synchronized swarms, according to Kallenborn, the George Mason researcher. So will future wars become a fight to the last drone? That’s what Putin predicted in a 2017 televised chat with engineering students: “When one party’s drones are destroyed by drones of another, it will have no other choice but to surrender.” —— Frank Bajak reported from Boston. Associated Press journalists Tara Copp in Washington, Garance Burke in San Francisco and Suzan Fraser in Turkey contributed to this report. —— Follow the AP’s coverage of the war at https://apnews.com/hub/russia-ukraine —— This story has been updated to correct when the U.N. report was issued. It came out in 2021, not last year. Postmedia is committed to maintaining a lively but civil forum for discussion. Please keep comments relevant and respectful. Comments may take up to an hour to appear on the site. 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Copyright 2026 The Associated Press. All Rights Reserved. Copyright 2026 The Associated Press. All Rights Reserved. FILE - A Switchblade 600 loitering missile drone manufactured by AeroVironment is displayed at the Eurosatory arms show in Villepinte, north of Paris, on June 14, 2022. Drone advances in Ukraine have accelerated a long-anticipated technology trend that could soon bring the world’s first fully autonomous fighting robots to the battlefield, inaugurating a new age of warfare. (AP Photo/Michel Euler, File) FILE - Ukraine’s Minister of Digital Transformation Mykhailo Fedorov gestures during a news conference at the Web Summit technology conference in Lisbon, Portugal, Thursday, Nov. 3, 2022. Fedorov, agrees that fully autonomous killer drones are “a logical and inevitable next step” in weapons development. (AP Photo/Armando Franca, File) FILE - Mykhailo Fedorov, Ukrainian minister of digital transformation, poses for a photo illuminated by a lamp in the shape of a Ukrainian trident, a national symbol, in his office in Kyiv, Ukraine, Wednesday, Dec. 21, 2022. Fedorov, agrees that fully autonomous killer drones are “a logical and inevitable next step” in weapons development. (AP Photo/Felipe Dana) FILE - This undated photograph released by the Ukrainian military’s Strategic Communications Directorate shows the wreckage of what Kyiv has described as an Iranian Shahed drone downed near Kupiansk, Ukraine. (Ukrainian military’s Strategic Communications Directorate via AP, File) FILE - A drone is seen in the sky seconds before it fired on buildings in Kyiv, Ukraine, on Oct. 17, 2022. (AP Photo/Efrem Lukatsky, File) FILE - Ukrainian soldiers shoot a drone that appears in the sky seconds before it fired on buildings in Kyiv, Ukraine, on Oct. 17, 2022. (AP Photo/Vadym Sarakhan, File) FILE - Ukrainian soldiers launch a drone at Russian positions near Bakhmut, Donetsk region, Ukraine, Thursday, Dec. 15, 2022. (AP Photo/LIBKOS, File) FILE - Firefighters work after a drone attack on buildings in Kyiv, Ukraine, Monday, Oct. 17, 2022. (AP Photo/Roman Hrytsyna, File) KYIV, Ukraine (AP) — Drone advances in Ukraine have accelerated a long-anticipated technology trend that could soon bring the world’s first fully autonomous fighting robots to the battlefield, inaugurating a new age of warfare. The longer the war lasts, the more likely it becomes that drones will be used to identify, select and attack targets without help from humans, according to military analysts, combatants and artificial intelligence researchers. That would mark a revolution in military technology as profound as the introduction of the machine gun. Ukraine already has semi-autonomous attack drones and counter-drone weapons endowed with AI. Russia also claims to possess AI weaponry, though the claims are unproven. But there are no confirmed instances of a nation putting into combat robots that have killed entirely on their own. Experts say it may be only a matter of time before either Russia or Ukraine, or both, deploy them. “Many states are developing this technology,” said Zachary Kallenborn, a George Mason University weapons innovation analyst. ”Clearly, it’s not all that difficult.” The sense of inevitability extends to activists, who have tried for years to ban killer drones but now believe they must settle for trying to restrict the weapons’ offensive use. Ukraine’s digital transformation minister, Mykhailo Fedorov, agrees that fully autonomous killer drones are “a logical and inevitable next step” in weapons development. He said Ukraine has been doing “a lot of R&D in this direction.” “I think that the potential for this is great in the next six months,” Fedorov told The Associated Press in a recent interview. Ukrainian Lt. Col. Yaroslav Honchar, co-founder of the combat drone innovation nonprofit Aerorozvidka, said in a recent interview near the front that human war fighters simply cannot process information and make decisions as quickly as machines. Ukrainian military leaders currently prohibit the use of fully independent lethal weapons, although that could change, he said. “We have not crossed this line yet – and I say ‘yet’ because I don’t know what will happen in the future.” said Honchar, whose group has spearheaded drone innovation in Ukraine, converting cheap commercial drones into lethal weapons. Russia could obtain autonomous AI from Iran or elsewhere. The long-range Shahed-136 exploding drones supplied by Iran have crippled Ukrainian power plants and terrorized civilians but are not especially smart. Iran has other drones in its evolving arsenal that it says feature AI. Without a great deal of trouble, Ukraine could make its semi-autonomous weaponized drones fully independent in order to better survive battlefield jamming, their Western manufacturers say. Those drones include the U.S.-made Switchblade 600 and the Polish Warmate, which both currently require a human to choose targets over a live video feed. AI finishes the job. The drones, technically known as “loitering munitions,” can hover for minutes over a target, awaiting a clean shot. “The technology to achieve a fully autonomous mission with Switchblade pretty much exists today,” said Wahid Nawabi, CEO of AeroVironment, its maker. That will require a policy change — to remove the human from the decision-making loop — that he estimates is three years away. Drones can already recognize targets such as armored vehicles using cataloged images. But there is disagreement over whether the technology is reliable enough to ensure that the machines don’t err and take the lives of noncombatants. The AP asked the defense ministries of Ukraine and Russia if they have used autonomous weapons offensively – and whether they would agree not to use them if the other side similarly agreed. Neither responded. If either side were to go on the attack with full AI, it might not even be a first. An inconclusive U.N. report suggested that killer robots debuted in Libya’s internecine conflict in 2020, when Turkish-made Kargu-2 drones in full-automatic mode killed an unspecified number of combatants. A spokesman for STM, the manufacturer, said the report was based on “speculative, unverified” information and “should not be taken seriously.” He told the AP the Kargu-2 cannot attack a target until the operator tells it to do so. Fully autonomous AI is already helping to defend Ukraine. Utah-based Fortem Technologies has supplied the Ukrainian military with drone-hunting systems that combine small radars and unmanned aerial vehicles, both powered by AI. The radars are designed to identify enemy drones, which the UAVs then disable by firing nets at them — all without human assistance. The number of AI-endowed drones keeps growing. Israel has been exporting them for decades. Its radar-killing Harpy can hover over anti-aircraft radar for up to nine hours waiting for them to power up. Other examples include Beijing’s Blowfish-3 unmanned weaponized helicopter. Russia has been working on a nuclear-tipped underwater AI drone called the Poseidon. The Dutch are currently testing a ground robot with a .50-caliber machine gun. Honchar believes Russia, whose attacks on Ukrainian civilians have shown little regard for international law, would have used killer autonomous drones by now if the Kremlin had them. “I don’t think they’d have any scruples,” agreed Adam Bartosiewicz, vice president of WB Group, which makes the Warmate. AI is a priority for Russia. President Vladimir Putin said in 2017 that whoever dominates that technology will rule the world. In a Dec. 21 speech, he expressed confidence in the Russian arms industry’s ability to embed AI in war machines, stressing that “the most effective weapons systems are those that operate quickly and practically in an automatic mode.” Russian officials already claim their Lancet drone can operate with full autonomy. “It’s not going to be easy to know if and when Russia crosses that line,” said Gregory C. Allen, former director of strategy and policy at the Pentagon’s Joint Artificial Intelligence Center. Switching a drone from remote piloting to full autonomy might not be perceptible. To date, drones able to work in both modes have performed better when piloted by a human, Allen said. The technology is not especially complicated, said University of California-Berkeley professor Stuart Russell, a top AI researcher. In the mid-2010s, colleagues he polled agreed that graduate students could, in a single term, produce an autonomous drone “capable of finding and killing an individual, let’s say, inside a building,” he said. An effort to lay international ground rules for military drones has so far been fruitless. Nine years of informal United Nations talks in Geneva made little headway, with major powers including the United States and Russia opposing a ban. The last session, in December, ended with no new round scheduled. Washington policymakers say they won’t agree to a ban because rivals developing drones cannot be trusted to use them ethically. Toby Walsh, an Australian academic who, like Russell, campaigns against killer robots, hopes to achieve a consensus on some limits, including a ban on systems that use facial recognition and other data to identify or attack individuals or categories of people. “If we are not careful, they are going to proliferate much more easily than nuclear weapons,” said Walsh, author of “Machines Behaving Badly.” “If you can get a robot to kill one person, you can get it to kill a thousand.” Scientists also worry about AI weapons being repurposed by terrorists. In one feared scenario, the U.S. military spends hundreds of millions writing code to power killer drones. Then it gets stolen and copied, effectively giving terrorists the same weapon. To date, the Pentagon has neither clearly defined “an AI-enabled autonomous weapon” nor authorized a single such weapon for use by U.S. troops, said Allen, the former Defense Department official. Any proposed system must be approved by the chairman of the Joint Chiefs of Staff and two undersecretaries. That’s not stopping the weapons from being developed across the U.S. Projects are underway at the Defense Advanced Research Projects Agency, military labs, academic institutions and in the private sector. The Pentagon has emphasized using AI to augment human warriors. The Air Force is studying ways to pair pilots with drone wingmen. A booster of the idea, former Deputy Defense Secretary Robert O. Work, said in a report last month that it “would be crazy not to go to an autonomous system” once AI-enabled systems outperform humans — a threshold that he said was crossed in 2015, when computer vision eclipsed that of humans. Humans have already been pushed out in some defensive systems. Israel’s Iron Dome missile shield is authorized to open fire automatically, although it is said to be monitored by a person who can intervene if the system goes after the wrong target. Multiple countries, and every branch of the U.S. military, are developing drones that can attack in deadly synchronized swarms, according to Kallenborn, the George Mason researcher. So will future wars become a fight to the last drone? That’s what Putin predicted in a 2017 televised chat with engineering students: “When one party’s drones are destroyed by drones of another, it will have no other choice but to surrender.” ___ Frank Bajak reported from Boston. Associated Press journalists Tara Copp in Washington, Garance Burke in San Francisco and Suzan Fraser in Turkey contributed to this report. ___ Follow the AP’s coverage of the war at https://apnews.com/hub/russia-ukraine ___ This story has been updated to correct when the U.N. report was issued. It came out in 2021, not last year. Copyright 2026 The Associated Press. All Rights Reserved.

Generative AI is reshaping industries and with accelerated computing, "we're pushing the boundaries of what's possible and driving the next wave of technological advancement," Nvidia CEO Jensen Huang (黃仁勳) said in a speech in Taipei Sunday. (Full text of the story is now in CNA English news archive. To view the full story, you will need to be a subscribed member of the CNA archive. To subscribe, please read here.)

Personalise the news and stay in the know Emergency Backstory Newsletters 中文新闻 BERITA BAHASA INDONESIA TOK PISIN Find any issues using dark mode? Please let us know Topic:Unrest, Conflict and War Ukrainian soldiers launch a drone at Russian positions near Bakhmut in Ukraine's Donetsk region in December 2022. (AP: LIBKOS/ File) Drone advances in Ukraine have accelerated a long-anticipated technology trend that could soon bring the world's first fully autonomous fighting robots to the battlefield, inaugurating a new age of warfare. The longer the war lasts, the more likely it becomes that drones will be used to identify, select and attack targets without help from humans, according to military analysts, combatants and artificial intelligence (AI) researchers. That would mark a revolution in military technology as profound as the introduction of the machine gun. Ukraine already has semi-autonomous attack drones and counter-drone weapons endowed with AI. Russia also says it possesses AI weaponry, though the claims are unproven. But there are no confirmed instances of a nation putting into combat robots that have killed entirely on their own. Experts say it may be only a matter of time before either Russia or Ukraine, or both, deploy them. "Many states are developing this technology," George Mason University weapons innovation analyst Zachary Kallenborn said. "Clearly, it's not all that difficult." The sense of inevitability extends to activists, who have tried for years to ban killer drones, but now believe they must settle for trying to restrict the weapons' offensive use. Ukraine's digital transformation minister Mykhailo Fedorov agreed that fully autonomous killer drones were "a logical and inevitable next step" in weapons development. He said Ukraine has been doing a lot of [research and development] in that direction. "I think that the potential for this is great in the next six months," Mr Fedorov said. Ukraine's Minister of Digital Transformation Mykhailo Fedorov believes that fully autonomous killer drones are "a logical and inevitable next step" in weapons development. (AP: Armando Franca/ File) Ukrainian Lieutenant Colonel Yaroslav Honchar, co-founder of the combat drone innovation nonprofit Aerorozvidka, said that human war fighters simply cannot process information and make decisions as quickly as machines. Ukrainian military leaders currently prohibit the use of fully independent lethal weapons, although that could change, according to Lieutenant Colonel Honchar, whose group has spearheaded drone innovation in Ukraine, converting cheap commercial drones into lethal weapons. "We have not crossed this line yet – and I say 'yet' because I don't know what will happen in the future," he said. Russia could obtain autonomous AI from Iran or elsewhere. The long-range Shahed-136 exploding drones supplied by Iran have crippled Ukrainian power plants and terrorised civilians, but are not especially smart. Iran has other drones in its evolving arsenal that it says feature AI. Without a great deal of trouble, Ukraine could make its semi-autonomous weaponised drones fully independent in order to better survive battlefield jamming, their Western manufacturers say. Those drones include the US-made Switchblade 600 and the Polish Warmate, which both currently require a human to choose targets over a live video feed. AI finishes the job. The drones, technically known as "loitering munitions", can hover for minutes over a target, awaiting a clean shot. That will require a policy change — to remove the human from the decision-making loop — that he estimates is three years away. From a shabby office in Hong Kong to a house in suburban Florida, these are the businesses allowing Russia to build drones that are killing Ukrainians. Drones can already recognise targets such as armoured vehicles using catalogued images. But there is disagreement over whether the technology is reliable enough to ensure that the machines don't err and take the lives of non-combatants. The Associated Press (AP) asked the defence ministries of Ukraine and Russia if they have used autonomous weapons offensively – and whether they would agree not to use them if the other side similarly agreed. Neither responded. If either side were to go on the attack with full AI, it might not even be a first. An inconclusive United Nations report suggested that killer robots debuted in Libya's internecine conflict in 2020, when Turkish-made Kargu-2 drones in full-automatic mode killed an unspecified number of combatants. A spokesman for the manufacturer STM said the report was based on "speculative, unverified" information and should not be taken seriously. He told the AP that the Kargu-2 cannot attack a target until the operator tells it to do so. Fully autonomous AI is already helping to defend Ukraine. Utah-based Fortem Technologies has supplied the Ukrainian military with drone-hunting systems that combine small radars and unmanned aerial vehicles, both powered by AI. The radars are designed to identify enemy drones, which the UAVs then disable by firing nets at them — all without human assistance. The number of AI-endowed drones keeps growing. Israel has been exporting them for decades. Its radar-killing Harpy can hover over anti-aircraft radar for up to nine hours waiting for them to power up. Other examples include Beijing's Blowfish-3 unmanned weaponised helicopter. Russia has been working on a nuclear-tipped underwater AI drone called the Poseidon. The Dutch are currently testing a ground robot with a .50-calibre machine gun. Lieutenant Colonel Honchar believes Russia, whose attacks on Ukrainian civilians have shown little regard for international law, would have used killer autonomous drones by now if the Kremlin had them. "I don't think they'd have any scruples," agreed Adam Bartosiewicz, vice-president of WB Group, which makes the Warmate. AI is a priority for Russia. President Vladimir Putin said in 2017 that whoever dominates that technology will rule the world. In a speech, Mr Putin expressed confidence in the Russian arms industry's ability to embed AI in war machines. (Reuters/ File) He stressed that "the most effective weapons systems are those that operate quickly and practically in an automatic mode". Russian officials already claim their Lancet drone can operate with full autonomy. "It's not going to be easy to know if and when Russia crosses that line," former director of strategy and policy at the Pentagon's Joint Artificial Intelligence Center Gregory Allen said. Switching a drone from remote piloting to full autonomy might not be perceptible. To date, drones able to work in both modes have performed better when piloted by a human, Mr Allen said. The technology is not especially complicated, top AI researcher from University of California-Berkeley professor Stuart Russell said. In the mid-2010s, colleagues he polled agreed that graduate students could, in a single term, produce an autonomous drone "capable of finding and killing an individual, let's say, inside a building", he said. An effort to lay international ground rules for military drones has so far been fruitless. Nine years of informal United Nations talks in Geneva made little headway, with major powers including the United States and Russia opposing a ban. The last session, in December, ended with no new round scheduled. Washington policymakers say they won't agree to a ban because rivals developing drones cannot be trusted to use them ethically. Toby Walsh, an Australian academic who, like Mr Russell, campaigns against killer robots, hopes to achieve a consensus on some limits, including a ban on systems that use facial recognition and other data to identify or attack individuals or categories of people. "If you can get a robot to kill one person, you can get it to kill a thousand." Scientists also worry about AI weapons being repurposed by terrorists. In one feared scenario, the US military spends hundreds of millions writing code to power killer drones. Then it gets stolen and copied, effectively giving terrorists the same weapon. To date, the Pentagon has neither clearly defined "an AI-enabled autonomous weapon" nor authorised a single such weapon for use by US troops, Mr Allen said. Drones are getting more stealthy, and detection technology is struggling to keep up. Now researchers are reporting a marked improvement in detection ranges, thanks to an unlikely source. Any proposed system must be approved by the chairman of the Joint Chiefs of Staff and two undersecretaries. That's not stopping the weapons from being developed across the US. Projects are underway at the Defense Advanced Research Projects Agency, military labs, academic institutions and in the private sector. The Pentagon has emphasised using AI to augment human warriors. The US Air Force is studying ways to pair pilots with drone wingmen. Former Deputy Defense Secretary Robert Work said in a report last month that it "would be crazy not to go to an autonomous system" once AI-enabled systems outperform humans — a threshold that he said was crossed in 2015, when computer vision eclipsed that of humans. Humans have already been pushed out in some defensive systems. Israel's Iron Dome missile shield is authorised to open fire automatically, although it is said to be monitored by a person who can intervene if the system goes after the wrong target. Multiple countries, and every branch of the US military, are developing drones that can attack in deadly synchronised swarms, according to Mr Kallenborn, the George Mason researcher. So will future wars become a fight to the last drone? That's what Vladimir Putin predicted in a 2017 televised chat with engineering students: "When one party's drones are destroyed by drones of another, it will have no other choice but to surrender". AP Topic:Accidents and Emergency Incidents Topic:Property Prices Topic:Government and Politics Topic:Drownings Topic:Explainer Topic:Unrest, Conflict and War Topic:Government and Politics Ukraine Unrest, Conflict and War Topic:Accidents and Emergency Incidents Topic:Property Prices Topic:Government and Politics Topic:Drownings Topic:Explainer Topic:Property Prices Topic:Unrest, Conflict and War Topic:Government and Politics Topic:Missing Person Your home of Australian stories, conversations and events that shape our nation. 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Technology Karan Kamble Oct 16, 2024 | Updated Nov 13, 2024, 02:26 PM GMT+5:30 Save & read from anywhere! Bookmark stories for easy access on any device or the Swarajya app. One fond childhood memory for kids growing up in the last century was watching the animated sitcom The Jetsons, where robotic hands for household chores were always abundant. It may have even sparked the fantasy in more than a few to have robots helping around the home one day. While robots — even those that look like humans, called ‘humanoid robots’ — were certainly around back then, they tended to be more of an experimental nature, where scientists and engineers were more concerned with tweaking a part here, a part there in order to simply identify and extend fundamental robotic capabilities. As time passed, robots grew more and more advanced, yet largely remained limited to an academic, scientific, or defence setting. However, key advancements in artificial intelligence (AI) over the last decade have promised to propel robots from experiments to helping hands, whether at a factory, a service or a retail outlet, or, as that childhood fantasy goes, in homes. Companies like Boston Dynamics, Tesla, Agility Robotics, Softbank Robotics, and Hanson Robotics are swiftly helping materialise and hopefully soon commercialise miraculously useful humanoid robots. Tesla’s suite of humanoid robot “friends,” showcased on 11 October 2024, provides a taste of things to come. Here in India, in the thriving tech city of Bengaluru, Karnataka, a two-member startup operating out of the co-founder’s apartment is working hard to move the robotics dial forward by designing and developing humanoid robots in India. While the global robotics giants boast of ever-improving, advanced robots that look surprisingly human and are capable of handling a variety of mundane tasks, the key differentiator with the Bengaluru initiative is set to be the price point. Whereas the market rate for humanoid robots presently is anywhere between $10,000 for the basic machines and around $100,000 for the top-of-the-line ones, the Bengaluru company that goes by the name of ‘Cosine Robots’ is targeting a $4,000 price point for their humanoid robot. The company has started piecing together the individual systems, starting with the actuators, to quickly commercialise them and use the resulting profits to build the rest of the parts until they all eventually add up to a full-fledged humanoid robot. For Cosine Robots co-founder Kiran Mathew Koshy, whose home doubles up as the office, building affordable humanoid robots is not just a personal business goal. He believes India must get a move on and develop robots that the Indian public can afford, lest the prices elsewhere make these next-generation robots largely inaccessible to the Indian market. “Even if we are not successful, someone will eventually succeed in building these robots at an affordable price. However, that price may not be affordable in India, even though it might be in the United States, Europe, or elsewhere,” he explains, adding, “It is crucial that we avoid this situation as a country by developing robots that are affordable for Indian industries, made in India, and deployable here.” The Come Up Koshy has done his professional time, accumulating close to a decade of experience working at multinational giants Amazon and Google. His work in these companies was standard software engineering fare, but he harboured the idea of starting up someday. “I've always wanted to do a startup, right from my college days, but I come from a very middle-class family, so that was not something I could jump into right away. Getting a job and getting a couple of promotions in the job was a natural progression,” he says. The time was right in mid-2023. The Covid-19 pandemic had passed. Enough professional experience had been gained. It was time to dust off those old startup dreams. Koshy had maintained a longstanding interest in robotics, but he was smart to notice that recent advancements in AI had changed the game. While building humanoid robots was no longer the dominant challenge, getting them to do stuff without having to program their every move remained a problem. Luckily, AI opened a pathway. In the pre-AI-infused robotics world, “you would use robotics only in a place where you have extreme levels of scale, where you can write the code once and then deploy it to, let's say, 10,000 robots at least,” Koshy says. “The only realistic use cases of robots were in things like the Amazon warehouse. And those are also not robotic arms or anything. Those are more like wheeled robots, which carry entire shelves so that the picking and packing is more convenient.” However, with advancements in AI, the need to hard-code the robot’s every move is fulfilled by AI’s ingenuity. So, AI can operate the robot and get useful work out of it. The AI advances that have propelled robotics development include the transformer-based architectures underlying large language models (LLMs) like ChatGPT, vision transformers, and the technology driving self-driving cars. They have enabled robots to be given instructions via text or speech, through which they scope out the area and the objects contained therein, and then proceed to execute tasks, like moving a glass of water from one place to another. Buoyed by these advances, the focus has shifted to making robots more accessible—specifically, by constructing them simply, swiftly, and at scale to enhance affordability. This is the mission Koshy and his co-founder Delip Thomas, a year senior to Koshy during their Bachelor of Technology studies at the Indian Institute of Technology (IIT), Patna, have undertaken. Building From Scratch Building a humanoid robot, broadly speaking, requires several systems, chief among which are the actuators, sensors, and AI and software. “We’re taking a ground-up approach where we build the hardware first, then add a vision stack that can recognise objects. After that, we will have an AI stack to execute actions based on instructions,” Koshy says. He and his colleague are starting off with the design and development of actuators. Actuators are essential because they enable robots to move. They convert energy into physical motion, enabling robots to walk, pick up objects, and make gestures. “You can think of an actuator as a single joint of the robot. We will soon start selling these actuators, and use the profits to finance building more robots,” Koshy says. Building actuators first is a good strategy because those currently on the market are expensive. A robot maker needs as many actuators as joints designed for the robot. This bumps up the overall cost of the robot. Cosine is, therefore, planning to make cheaper actuators, which will help them lower the cost of their eventual robot offerings. “A single joint actuator on the market today would be around $1,000 plus. I'm talking about precise ones that you would use in industrial arms. We are building ours to be priced between $300 and $400,” Koshy says. The joints of a robot must be interconnected, which brings us to the static components that connect the actuators. Again, with affordability in mind, Koshy and his colleague plan to employ mass-manufacturing techniques like casting and sheet metal bending and cutting to make the static components, borrowing from the ways of automobile manufacturing, where affordable and scale-friendly methodologies are used. “In a car, you have several components which have a very high level of precision, like the engine, crankshaft, or, in an electric car, the motor. Everything else, like the body and chassis, just has to be strong; they don't have to be very precise. It's the precision that raises the cost. The components that need to be precise need to be manufactured using machining or milling, whereas sheet metal operations and stamping, for example, are a lot cheaper and faster,” he says. Similarly, while building robots, the components demanding precision can be machined, whereas the other parts, like the static components, can be made relatively quickly and affordably, like in car manufacturing. Koshy has gone about designing their actuators in such a way that they could be made on the standard automotive original equipment manufacturer (OEM) platforms. “Say, someone who builds pistons for a Maruti Suzuki engine would be able to build actuators for this, except the controller,” he says. Actuators aside, the Bengaluru-based robot makers will also need sensors, along with AI and software. Koshy plans to use the standard Hall effect sensors procured off the shelf. A Hall effect sensor measures the strength and direction of a magnetic field. In the automotive industry, it is used in wheel speed sensors (ABS systems), camshaft or crankshaft position sensors, and throttle position sensors. A more familiar application would be using the sensor to detect the opening and closing of flip cases on our smartphones or laptop lids by sensing embedded magnets. As for the AI stack, Cosine Robots plans to opt for existing off-the-shelf or open-source LLMs. They recognise the need to build AI models of their own at some point, but that’s for later. For now, the focus is on getting their self-made actuators off the ground. Among other things, the ‘actuator first’ strategy will enable the bootstrapped initiative to generate initial revenue, allowing them to pursue funding to gradually enhance their robot offerings. The startup’s earlier attempts to woo investors came to nought. “Deep tech investors have had their hands burnt by a lot of robotics startups. Also, there aren’t really that many deep tech investors in India to begin with. There are some, but they have a policy of not funding until there is some level of revenue, which is hard to get in deep tech before you build a product — where significant risk capital is needed first. This creates a gap in the VC (venture capital or capitalists) market in India.” Therefore, Koshy plans to generate revenue by selling actuators first. This will allow him to deploy capital the way he sees fit. “At that point, if we get investors, that will be even better. But we are not entering a path where we need to have VCs deploy capital before we can do the work,” he says. The good news is that there are takers for Cosine’s actuators already. “We have a couple of pre-orders from people looking to use our actuators. We will start selling them as quickly as we can, probably in the next month.” Building Robots That People Can Buy Cosine is not out to make the most advanced robots. Rather, they are in the business of making robots that most people can buy. Explaining the current state of robotics, Koshy states, “We are not in a phase where the bottleneck is in creating something that doesn’t exist. Instead, we are at a stage where many of these technologies have been developed by various people historically, some even 20 or 30 years ago. However, they are produced at such a low scale and volume today that the per-unit costs are astronomically high. Everyone is racing to reduce this price as much as possible while remaining profitable.” The pursuit of affordable humanoid robots ties into Koshy’s dream of a world, particularly India, where luxuries typically affordable only to the well-off, such as hiring a housekeeper, cook, or driver to make life easier, become accessible to nearly anyone. “It doesn't matter how rich the entire world becomes, but, in our current state of technology, someone in the bottom 50 per cent is never going to have a cook or a maid. Whereas in a world with, let's say, 10 billion robots, this is very trivial. Everyone can then have a robotic cook or a robotic maid to clean up the apartment, and maybe robotic security so that women are safe.” According to Koshy, a society with affordable robots will be as different from one without them as society was before and after the Industrial Revolution. “Imagine a world where you can do construction with robots. You can easily provide five-bedroom apartments for everyone. A lot of the things that are wrong in society are primarily because we don’t have infrastructure. We don't have good roads; we don't have good houses for everyone. These are very simple labour problems which immediately go away once you have affordable robots.” Cosine Robots have begun this journey. They have built a prototype humanoid robot — think of it as Version 1 — that was put together using off-the-shelf motors. The more capable, the more indigenous Version 2 will incorporate its own actuators. Once the robot is ready for the market, the company will initially target industrial settings. This environment will provide controlled real-world experience before the robots are deployed in homes. “In the industry, the person using the robot has full control of the setting and where it is used, so they can easily have a walled-off area where the robot operates, and no one in their right mind will walk into that area. However, in a home environment, you can't really do that if you have pets or your kids running around,” Koshy explains. The home version of the robot will simply be the industry version with additional safety features built into it. “Safety controls would be things like contact sensors, which can detect skin touch on the robot the same way a capacitor screen on your phone can detect a skin touch. Similarly, you can have similar sensors around the robot’s body so that it stops whenever a human touches it. Everything required for home safety is industrial plus some safety stuff,” he says. Sociopolitical Implications Any talk of introducing robots in greater numbers in society typically accompanies some nervousness. The resulting job losses are perhaps the most prominent concern. According to Koshy, though some jobs would be lost, especially those that “involve pressing some buttons,” new opportunities will open up in a society that welcomes humanoid robots. “When we started introducing computers, there were a lot of people who started protesting because they feared the loss of jobs. But the reality is, in India, computers enabled us to do a lot of work and generate a lot of income as a country, primarily by providing service work to other nations,” he says. Koshy argues that a similar scenario could unfold in India's robotics sector. “If you have a robot in the United States (US) and you have someone in India who can control that robot, there is quite a lot of work which you can do remotely, even without AI, even without any automation, by simply doing manual control.” So, through remote robot control, someone in India could help run an American factory at night. Moreover, robotics and especially AI will shake things up in ways we can’t quite grasp just yet. With such an unwritten future ahead of us, where chapters are waiting to be written, India can’t afford to hang back and wait for other authors to do the writing. “I would give an example of what happened to the Arab nations after the printing press was introduced. The Arab nations banned the printing press because they wanted to save the jobs of the royal scribes. What happened as a result of the printing press was a massive increase in living standards in the West, whereas the living standards in most of Asia pretty much remained the same,” Koshy says. “In the long term, things are 100 per cent going to be better. In the short term, will we have turmoil as a result? Yes. Do we need to adjust as a society? Yes,” he adds. As is often the case, governments will have to play a key role. “The best way to tackle it is to ease the pain of people whose jobs are getting displaced without entering full-fledged socialism. So, you still need capitalism, but you can reduce capitalism from all segments to some segments.” Drawing a distinction between the paths taken by Europe and Russia on one hand and the US on the other after the Industrial Revolution, Koshy says, “There was also a very high increase in productivity compared to what was there earlier. In Russia, they decided, ‘Hey, we have reached peak productivity. Let’s just distribute the gains and enter into socialism.’ The problem with that approach is that it effectively caps your growth, resulting in further productivity at a very low rate once you enter socialism.” Koshy’s view is that a mixed economy where socialist elements are added on top of a capitalist system would be the way to go. It will retain the incentive to pursue further increases in productivity while providing a safety net for those in need of protection. The introduction of the Social Security Act in the US in the 1930s serves as an example of this approach, in contrast to the paths taken by Europe and Russia after the Industrial Revolution. Koshy believes that in the age of robotics and AI, governments can harness technology to ensure that even if someone is without a job, they are not without food to eat or a place to live. Widespread use of robotics can make that happen. Karan Kamble writes on science and technology. He occasionally wears the hat of a video anchor for Swarajya's online video programmes. Join our WhatsApp channel - no spam, only sharp analysis Comments ↓ About Swarajya Shaping the modern Indian's worldview, speaking on behalf of those invested in the cultural and economic prosperity of India. Published since 1956. Swarajya is a publication by Kovai Media Private Limited. Useful Links Useful Links Participate Stay Connected

Business Naver to debut new OS for robots at Saudi tech show Published : March 5, 2024 - 17:07:26 Link copied! Korean internet giant Naver said Tuesday that it is debuting the world’s first web platform-enabled operating system for robots, called “Arc mind,” at this week’s LEAP 2024, a tech trade show held in Saudi Arabia. The company said the new platform combines the web platform technology of Naver Cloud’s Whale and the robotics software technology of Naver Labs. "Team Naver has continuously expanded its tech portfolio from web platform to robotics software by developing and operating Whale browser, Whale OS, and the multi-robot intelligence system Arc (AI-Robot-Cloud). Such technical capabilities became the basis for Arc mind, which converges advanced technologies of the future," Naver Cloud General Leader Kim Hyo said at a press briefing in Seoul on Tuesday. "Arc mind will create the optimal environment to develop and provide a wider variety of robots and robotic services to future cities. This will open up a new field of robotics to web developers around the world and speed up the mass adoption of robotics in our everyday lives." Peck Jong-yoon, Naver Labs executive officer, also explained how its new OS aims to compete with Robot Operating System, the widely used open-source robotics OS. "We do want to build a new ecosystem similar to Android, but it's not the same. The difference is that it is not creating an entirely new OS, but using the existing web platform. Web platforms are universal and it is excellent in compatibility and productivity,” Peck said. To introduce a new service like a payment system for a delivery robot, the developers would have to start from scratch on most occasions. But with Arc mind, they can use existing payment systems such as Naver Pay to speed up the overall development process. Naver said it plans to implement Arc mind on its own robots first, with the ultimate plan of expanding it to a fully open ecosystem. At LEAP 2024 running from Monday to Thursday, Naver Labs CEO Seok Sang-ok plans to deliver a keynote speech, titled “Tech Convergence for Future Cities,” and the company has set up an exhibition booth to showcase its latest technologies such as digital twin, AI, cloud and Arc mind. The firm also plans to debut a new robotics platform, called Robotics Edge Computing Platform, a collaboration with Samsung Electronics. The new platform combines Samsung’s chip solutions and Naver’s software in a bid to accelerate the wider adoption of cooperative robots, the company added. Strong exports, weak currency: Why the won can’t catch a break The Korean won is expected to remain under pressure throughout 2026 as it confronts a new set of forces reshaping global financial hegemony — most notably a US-led artificial intelligence boom that is New year brings host of new rules More than 80% of South Koreans watch streaming Yearlong exercise halves depression risk: study [Graphic News] Year of the Fire Horse Russia, North Korea exchange New Year's gifts Diplomatic Circuit North Korean POWs’ transfer to S. Korea still pending Diplomatic Circuit K-pop 101 The world of K-pop explained, for both fans and newcomers Inside Gen Z Giving Korea's new generation a voice Oddities From the funny to the strange and downright unbelievable Herald Interview A series of in-depth interviews. Coupang’s W1.7tr payout plan fails to quell public anger Global banks see 1,400 won as new baseline What Georgia raid changed for journalists heading to CES Samsung develops first inhouse mobile GPU for Exynos 2600 Coupang founder apologizes for data breach amid mounting scrutiny Korea wants foreign students, but does it want the graduates? AI is no longer optional in K-pop — it’s becoming the new normal [Graphic News] Year of the Fire Horse Veteran actor Ahn Sung-ki in critical condition Fans mark BTS’ V’s 30th birthday with stone bench at Versailles Address : Huam-ro 4-gil 10, Yongsan-gu,Seoul, Korea Tel : +82-2-727-0114 Online newspaper registration No : Seoul 아03711 Date of registration : 2015.04.28 Publisher. Editor : Choi Jin-Young Juvenile Protection Manager : Choi He-suk The Korea Herald by Herald Corporation. Copyright Herald Corporation. All Rights Reserved.

Want to read in a language you're more familiar with? ByteDance releases GR-2 robot AI large model, with an average task completion rate of 97.7%, simulating human learning to handle complex tasks. The research team of ByteDance has recently launched the second-generation large-scale model GR-2 (Generative Robot 2.0). Its highlight lies in innovatively constructing the "robot infancy" learning stage, imitating human growth and learning complex tasks, possessing outstanding generalization ability and multitask versatility. Like many other AI models, the GR-2 model undergoes two processes: pre-training and fine-tuning. During the pre-training phase, GR-2 'watched' up to 38 million internet videos from various public datasets and 500 billion tokens, covering a wide range of daily scenes such as home, outdoor, and office environments. This enables GR-2 to have generalization capabilities across a wide range of robot tasks and environments in subsequent reinforcement learning. In the fine-tuning stage, the team used robot trajectory fine-tuning for video generation and action prediction, demonstrating outstanding multitasking capabilities, achieving an average success rate of 97.7% in over 100 tasks. In addition, GR-2 demonstrates excellent generalization ability in novel and previously unseen scenarios, including new backgrounds, environments, objects, and tasks. SEE ALSO: ByteDance’s Doubao Video Generation Large Model Released Related posts coming soon... Pandaily is a tech media based in Beijing. Our mission is to deliver premium content and contextual insights on China's technology scene to the worldwide tech community. © 2017 - 2026 Pandaily. All rights reserved.

A humanoid robot has stunned the tech world by nailing complex drum performances with over 90 percent rhythmic accuracy, even executing humanlike techniques such as stick switching and cross arm hits. Developed by researchers at SUPSI, IDSIA, and Politecnico di Milano, this “Robot Drummer” system relies on reinforcement learning rather than preprogrammed sequences, marking a milestone in machine creativity and physical coordination. Instead of feeding the robot exact instructions, its creators transformed drum scores into “Rhythmic Contact Chains” precise sequences of timed contact events and trained the model on over 30 rock, metal, and jazz tracks. In simulation, the robot mastered rhythmic timing while developing expressive behaviors such as planning strike patterns and adapting stick use spontaneously. The idea originated from a casual chat between researchers who recognized that humanoid robots rarely venture into the expressive realm of music. Drumming became a natural test case for combining rhythm, coordination, and creative expression. The team believes the robot could eventually perform live alongside human musicians. The team’s next ambition is transferring these simulated drumming skills to actual hardware, empowering the robot to improvise live based on musical cues essentially allowing it to respond to its environment just like a human drummer. Figures humanoid robots now march naturally using reinforcement learning trained in simulation and Boston Dynamics is enhancing agility through similar AI driven methods. This reflects a growing movement away from manual programming toward autonomous learning and adaptability in robotics. Drumming demands split second precision, full body coordination, and dynamic timing traits essential to musical artistry. That a robot can emulate these human qualities opens new doors for both entertainment and educational applications. Whether guiding music students or performing on global stages, robotic musicians may no longer be sci fi. Abdul Wasay explores emerging trends across AI, cybersecurity, startups and social media platforms in a way anyone can easily follow. The federal government and NADRA have officially amended the National Identity Card (NIC) Rules to widen the legal definition of biometrics. As of Wednesday, facial. Russia has introduced a new quantum computer prototype, signaling tangible progress in its long running effort to build advanced computing systems without relying on foreign. Taiwan Semiconductor Manufacturing Company has quietly begun volume production of its first 2nm class chips, marking a major manufacturing milestone as the world’s largest contract. Researchers have developed a new physical artificial intelligence system that enables electric vehicles to detect loss of control in real time by combining traditional physics-based. Premier Pakistan technology news website with special focus on startups, entrepreneurship and consumer products. © 2025 TechJuice.PK – All rights reserved.

From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Explore The Most Powerful Tech Event in the World with Interesting Engineering. Stick with us as we share the highlights of CES week! Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies. We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. The robot uses a combination of machine learning and a flexible “back” to chimney climb with ease. A team of researchers from Japan’s Jouhou System Kougaka Laboratory at the University of Tokyo (JSK) has developed a new robot that can both walk on uneven terrain and climb vertical walls. Called KLEIYN, the robot can scale vertical surfaces using a technique called chimney climbing (pressing its legs against two opposing walls for support). To support their paper on the subject, published in the journal arXiv, the team has also released footage of KLEIYN in action. The robot itself is a four-legged (quadruped) robot with an active waist joint (it can bend in the middle). According to its developers, the robot uses quasi-direct-drive motors for precise and powerful movement. All in all, the robot weighs approximately 40 pounds (18 kg), has 13 joints, and measures around 2.5 feet (76 cm) in length. Furthermore, the robot utilizes a form of machine learning called Reinforcement Learning (RL) in simulation to learn how to move and climb. The team also integrated a special training method called Contact-Guided Curriculum Learning (CGCL), which gradually teaches it how to transition from flat ground to vertical surfaces. Other technical innovations include Asymmetric Actor-Critic RL, an efficient training setup that enables the robot to learn from rich simulation data while utilizing only basic sensors in real-life applications. What makes KLEIYN different from the many other climbing robots out there is the design of its “feet.” Most climbing robots use grippers (like claws) to grab onto things, which limits their walking ability. Instead, KLEIYN uses chimney climbing, which works by pressing its feet against two walls, eliminating the need for grippers. The waist joint lets it adapt to different wall widths, especially narrow ones. This design appears to have paid off remarkably well. During test climbs of walls spaced 31.5 inches (80 cm) to 39.4 inches (1 m) apart, the robot was able to climb at a rate of 6 inches (15 cm) to 6.7 inches (17 cm) per second. That is roughly 50x faster than the previous best (SiLVIA). The robot was also able to walk on rough terrain and climb steps outdoors successfully. The robot can learn how to recover from slipping, making it more robust. But not all is going good for KLEIYN. According to the team, it struggles with any wall gaps wider than 77 inches (1.05 m) due to torque limits. It also moved sideways unintentionally during climbs, meaning it needs better environmental sensing (e.g., LiDAR input). The robot’s motors also tend to overheat during long climbs, suggesting a need for better load balancing. That said, this research pushes the boundaries of robot mobility, enabling one robot to handle both flat ground and vertical obstacles. That could make it valuable for tasks such as search and rescue in collapsed buildings, exploration of complex environments (like caves or disaster zones), and transportation tasks in uneven terrain. You can view the study for yourself in the journal arXiv. You can also check out the design team’s GitHub page for more information on the project. Christopher graduated from Cardiff University in 2004 with a Masters Degree in Geology. Since then, he has worked exclusively within the Built Environment, Occupational Health and Safety and Environmental Consultancy industries. He is a qualified and accredited Energy Consultant, Green Deal Assessor and Practitioner member of IEMA. Chris’s main interests range from Science and Engineering, Military and Ancient History to Politics and Philosophy. Premium Follow

0 By clicking the button, I accept the Terms of Use of the service and its Privacy Policy, as well as consent to the processing of personal data. Don’t have an account? Signup Karnataka teacher Akshay Mashelkar wanted to make education more interactive for children in his village, and so he built Shiksha, a humanoid robot that imparts education to children under Class 4. Karnataka teacher Akshay Mashelkar wanted to make education more interactive for children in his village, and so he built Shiksha, a humanoid robot that imparts education to children under Class 4. google-news Follow Us Dressed in a blue shirt and tunic, with neatly parted hair styled into two pleats, the humanoid robot named ‘Shiksha’ bears a striking resemblance to the rest of the students of Sirsa village. As she begins delivering the day’s lessons — from rhymers to the days of the weeks, names of different shapes, and more — there’s a sense of wonder in the eyes of each student as they take in this remarkable teaching experience. Shiksha is the brainchild of 30-year-old Akshay Mashelkar, and aims to make learning fun and interactive “Growing up in a village I was very aware of the limitations of schools in rural areas. We still use printed charts and blocks as a means of learning. There are no scientific methods available. I want to change that,” Akshay tells The Better India. Born and raised in the village of Sirsi in Karnataka’s Uttara Kannada district, Akshay grew up in a teaching household. “My mother was a teacher and from a very young age, I knew I wanted to become an educator too. While studying, I realised that I wanted to work towards improving the education system,” he says. Following in the footsteps of his mother, Akshay became a professor at a college in Sirsi after completing his degree in Physics. “While I enjoyed my job as a professor, I had many ideas to implement in the education system. With the work, there was no time for me to start working on it though,” he says. When the COVID pandemic hit and the education sector moved online, Akshay found himself relatively free. “I found the perfect opportunity to work on my ideas. One of the most important things that I have seen in the education sector, especially in Tier-2 and -3 cities and rural areas, is the lack of modern and scientific methods of teaching. On one of my several visits to schools in the village, I saw that teachers were still using charts and blocks to teach,” he says. “Those techniques were used when I was in school. It is sad that the world has advanced so much with smart boards and whatnot, but schools in rural areas are still stuck with handmade charts. This pushed me further to give all my attention to bringing an easier and cheaper solution,” he adds. It took Akshay a good one and half years to do the research. In 2022, ‘Shikha’ — a humanoid robot capable of teaching in regional languages up to Class 4 — was ready. In India, the education sector has been incorporating technology for teaching purposes for several years. Nevertheless, its implementation has primarily been observed in urban regions and expensive schools. On the contrary, rural schools continue to rely on conventional tools like charts and drawings to facilitate learning. Moreover, teachers of government schools are overburdened with students. A recent Quint report states, “The number of teachers in government schools in Karnataka has dropped from 2.08 lakh to 1.99 lakh, forcing 6,529 schools in the state to have only one teacher. The student-teacher ratio is now 23:1 when compared to 21:1 in 2020-21.” The inclusion of such a device could help fix this problem. The robot took nearly Rs 2 lakh to build, which he took out of his own savings. “A lot of money was involved in the research and development. On average, making only a robotic arm costs nearly Rs 50,000. ‘Shiksha’ is an entire robot with several features. The reason why I was able to cut costs was I used jugaad. For instance, I did not use a mould for the body of the robot, instead for the arms I used plastic cricket stumps that you find in toy shops,” he says. Siksha can teach various subjects including rhymes in Kannada and English rhymes; the days of the week; names of shapes; English alphabets, and maths topics such as multiplication, addition and tables. Explaining how the robot works, Akshay says, “The robot has two main cards — the master card that unlocks it, and the normal card to start the desired programme. The teacher has to put the master card on Shiksha’s hand to start it and then they can use the programme cards to start different programmes. She moves her arms to take the card and returns it once scanned. She asks questions, recites poems, and even has trivia options,” he says. The robot has visited over 25 schools in the Uttar Kannada district, including KHB School and Urdu School in Sirsi. So far, Shiksha can teach up to Class 4 and has syllabus accommodation across boards. Sunaina Hegde, who teaches Science and Maths at Model Higher Primary School in Sirsi, says, “Akshay came with Shiksha to our school in April. The children were so happy to see her and they took a greater interest in the class. For them, Shiksha was not a robot, but more like a friend as it was dressed like them too.” “While it is great for students to learn, it is also a great tool to be included in schools for teachers. It reduces our burden, as there are fewer teachers in government schools. Something so interactive helps children to gain more interest in science and technology,” she adds. Akshay notes, “The importance of involving village children in technology is because they are also the future of the country. An average child living in an urban setting, from a very young age, knows how to operate laptops and computers. Sadly, this is not true for kids in rural areas. When the kids saw Shiksha for the first time, I could see the sparkle in their eyes. They were intrigued, amazed and excited.” “My motive behind making Siksha was not only to introduce technology in the classroom but also to encourage children to make their robots,” he says. Taking the thought forward, Akshay also opened a research centre where young robotics enthusiasts can come and learn for free. “In order to keep the cost of the operations of the centre low, we keep our centre mobile. Whenever we get low-cost places to rent in Sirsi, we move to that place. Over 200 children have visited the centre and many are regulars now. They have the space to learn from me and use the tool available in the research centre,” he says. Although the first Shiksha cost him lakhs, Akshay says that he can reduce the cost even more. “There were a lot of errors and a lot of investment in R&D initially, but now there won’t be. With the help of grants and support from the government and NGOs, I can possibly reduce the cost to Rs 35,000. This way it will be cheaper to afford rural schools. My only wish is to take Shiksha to every rural school in Karnataka and make learning fun,” he adds. If you wish to know more about his research centre and be a part of his initiative, you can reach him at 74832 76508. (Edited by Divya Sethu) Subscribe to our Newsletter! Quick Links

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Digital Replica Edition Sign up for Newsletters and Alerts Sign up for Newsletters and Alerts Digital Replica Edition Trending: LEGO has been making childhood toys for more than 50 years and now even offer kits that allow your child to build a robot. These sets encourage young kids to explore and get excited about STEM. LEGO robot kits are great gifts that combine fun with learning to build, code and program. They come in variations designed for every age and skill level. If you or your child wants to build a fully functional intelligent robot that walks, talks, plays games and completes many different tasks, try the LEGO Mindstorm EV3 31313 Robot Kit. Start by choosing one of two main types of robot kits: model robots or programmable robots. The greater the detail involved, the more time and effort required to build the robot. Programmable robot kits will always be more detailed than model robot kits. Look for two key indicators: the number of pieces included and the suggested age range. More detailed and complicated kits will have at least 500 separate pieces. Robot kits with high levels of detail will require tools to assemble and will also be more expensive. Small programmable robot kits have one motor while larger and more detailed robot kits will have two or even three motors. High-tech add-ons like transmitters and sensors have electrical components that need more than one motor to operate properly. You will find simple model kits for $15-$25 and more detailed ones for $100 or more. Programmable model kits start at around $150 and increase in cost as you add pieces, motors, sensors and possibilities. A. Yes, on two different levels. LEGO Boost kits teach younger kids the basic rules of programming through the use of drag-and-drop icons. Those who choose LEGO Mindstorm kits learn how to code by writing their own programs using more complex processes. A. Anyone can have fun with a LEGO robot kit. If you enjoy technology, try using a more complex model. If you’re new to coding, a simpler kit is a good start. LEGO Mindstorm EV3 31313 Robot Kit What you need to know: This fully functional intelligent robot kit allows you to create five different robots that walk, talk and play games. What you’ll love: Builders of all ages will enjoy creating and commanding their own 16- by 15- by 14-inch robot with this 601-piece kit. It includes the intelligent EV3 Brick, three motors and color, touch and infrared sensors. What you should consider: This is a pricey robot that requires some technical and programming skills. LEGO Star Wars VIII BB-8 75187 Building Kit What you need to know: This Star Wars droid has more than 1,100 parts and is designed for kids 10 to 16. What you’ll love: The detailing is authentic. Turn one wheel at the side to rotate the head and another to open the access hatch and extend the welding torch. It also comes with a display stand, decorative fact plaque and mini-figure. What you should consider: This kit is expensive for a model that can’t be programmed. Prices listed reflect time and date of publication and are subject to change. Check out our Daily Deals for the best products at the best prices and sign up here to receive the BestReviews weekly newsletter full of shopping inspo and sales. BestReviews spends thousands of hours researching, analyzing and testing products to recommend the best picks for most consumers. BestReviews and its newspaper partners may earn a commission if you purchase a product through one of our links. Copyright © 2026 MediaNews Group

e-Edition Get the latest news delivered daily! Get the latest news delivered daily! e-Edition Trending: LEGO has been making childhood toys for more than 50 years and now even offer kits that allow your child to build a robot. These sets encourage young kids to explore and get excited about STEM. LEGO robot kits are great gifts that combine fun with learning to build, code and program. They come in variations designed for every age and skill level. If you or your child wants to build a fully functional intelligent robot that walks, talks, plays games and completes many different tasks, try the LEGO Mindstorm EV3 31313 Robot Kit. Start by choosing one of two main types of robot kits: model robots or programmable robots. The greater the detail involved, the more time and effort required to build the robot. Programmable robot kits will always be more detailed than model robot kits. Look for two key indicators: the number of pieces included and the suggested age range. More detailed and complicated kits will have at least 500 separate pieces. Robot kits with high levels of detail will require tools to assemble and will also be more expensive. Small programmable robot kits have one motor while larger and more detailed robot kits will have two or even three motors. High-tech add-ons like transmitters and sensors have electrical components that need more than one motor to operate properly. You will find simple model kits for $15-$25 and more detailed ones for $100 or more. Programmable model kits start at around $150 and increase in cost as you add pieces, motors, sensors and possibilities. A. Yes, on two different levels. LEGO Boost kits teach younger kids the basic rules of programming through the use of drag-and-drop icons. Those who choose LEGO Mindstorm kits learn how to code by writing their own programs using more complex processes. A. Anyone can have fun with a LEGO robot kit. If you enjoy technology, try using a more complex model. If you’re new to coding, a simpler kit is a good start. LEGO Mindstorm EV3 31313 Robot Kit What you need to know: This fully functional intelligent robot kit allows you to create five different robots that walk, talk and play games. What you’ll love: Builders of all ages will enjoy creating and commanding their own 16- by 15- by 14-inch robot with this 601-piece kit. It includes the intelligent EV3 Brick, three motors and color, touch and infrared sensors. What you should consider: This is a pricey robot that requires some technical and programming skills. LEGO Star Wars VIII BB-8 75187 Building Kit What you need to know: This Star Wars droid has more than 1,100 parts and is designed for kids 10 to 16. What you’ll love: The detailing is authentic. Turn one wheel at the side to rotate the head and another to open the access hatch and extend the welding torch. It also comes with a display stand, decorative fact plaque and mini-figure. What you should consider: This kit is expensive for a model that can’t be programmed. Prices listed reflect time and date of publication and are subject to change. Check out our Daily Deals for the best products at the best prices and sign up here to receive the BestReviews weekly newsletter full of shopping inspo and sales. BestReviews spends thousands of hours researching, analyzing and testing products to recommend the best picks for most consumers. BestReviews and its newspaper partners may earn a commission if you purchase a product through one of our links. Copyright © 2026 MediaNews Group

Fasciné par le fonctionnement complexe du cerveau animal, Thomas Deneux, chercheur au CNRS, décide de mettre au point une IA permettant de visualiser des réseaux de neurones. Il fonde alors Learning Robots, startup qui propose un logiciel capable d'entraîner un robot pour des activités simples et de visualiser le fonctionnement de ses neurones, et s’associe avec Axel Haentjens. L’objectif ? Sensibiliser le grand public à l’IA en la démystifiant grâce à une vision claire sur ses mécanismes et son apprentissage. « Avec la généralisation des chatbot et des IA génératives comme Chat GPT, l’IA est partout, sauf que peu de gens comprennent comment cela fonctionne ! », rappelle Axel Haentjens, Directeur Général de Learning Robots. « Nous voulons changer la vision du public sur l'IA, la démystifier et la rendre accessible à tous, et pas seulement à une élite. Or, c'est en manipulant et en expérimentant que l'on apprend le mieux.» Grâce à un logiciel, il est possible de visualiser en temps réel les décisions prises par un petit robot, l’AlphAI, et d’expliquer les processus d'algorithmes sous-jacents qui permettent l’apprentissage de la machine. Cette approche rend l'IA tangible et compréhensible, sans concepts abstraits souvent difficiles à saisir, grâce à la mise en pratique. N’importe quel public peut comprendre le fonctionnement de l’IA, comment ses neurones fonctionnent et même observer comment le robot « voit » et prend ses décisions. Les évolutions du robot, au fur et à mesure de son apprentissage, permettent de mieux comprendre l’importance de la qualité des données dans son évolution. « L’IA se trompe beaucoup au début. Mais plus elle apprend, mieux elle va fonctionner. Cela montre aux enfants qu’il est normal de faire des erreurs, pour mieux comprendre et apprendre », précise le Directeur Général. « En entraînant ce robot, tout le monde peut comprendre ce qu’est un algorithme. On peut commencer à l’utiliser dès la classe de sixième, pour sensibiliser à l’IA, puis en seconde, on peut rentrer dans le détail des calculs des algorithmes, et enfin dans le supérieur, commencer à coder ces algorithmes. » La jeune pousse vend des kits robotiques et des licences logicielles pour le suivi et l'entraînement des robots aux établissements. Plusieurs centaines d’établissements, comme l’Université Paris-Saclay, utilisent déjà cette technologie. Néanmoins, si Learning Robots s’intègre parfaitement dans le système éducatif, elle a su trouver sa place également dans le milieu professionnel : des entreprises, notamment du CAC 40, utilisent déjà cette technologie pour former leurs cadres et les aider à comprendre l’IA et réduire leurs appréhensions. Après avoir remporté le Prix International Next Innov by Banque Populaire Val de France, la startup souhaite désormais s’étendre à l’international pour développer de nouvelles fonctionnalités, notamment la connexion de son logiciel à des modèles de langage génératif et ainsi faciliter les interactions homme-machine. Des discussions sont en cours pour établir des partenariats à Hong Kong, en Suisse, au Brésil, en Allemagne et au Royaume-Uni. « Lorsque nous avons entendu parler de Next Innov, j’ai directement flashé sur le nom : Next, c’est préparer le coup d’après. Et innover, c’est notre ADN », conclut Axel Haentjens. « Au-delà des aspects financiers, ce prix va nous permettre d’obtenir une certaine notoriété, la Banque Populaire Val de France étant très connue, et de disposer d’un rayonnement médiatique certain, d’un accompagnement et d’un réseau : ce sont des rencontres qui entraînent des rencontres, et cela est passionnant ».

In a striking demonstration of robotic agility, Tesla’s Optimus humanoid robot has been captured on video executing a series of kung fu moves with remarkable fluidity, mirroring the motions of a human trainer. Shared by Elon Musk on the social platform X, the 36-second clip shows Optimus blocking strikes, dodging attacks, and performing precise hand gestures, all while maintaining balance on a mat. This isn’t just a parlor trick; it highlights significant advancements in Tesla’s AI-driven robotics, where the machine learns complex physical tasks through imitation and self-correction, without direct human intervention during the performance. The video, which has garnered millions of views, underscores Optimus’s evolution from earlier prototypes that struggled with basic locomotion to a system capable of nuanced, dynamic interactions. Musk emphasized that the robot’s actions are powered entirely by artificial intelligence, with no teleoperation or pre-programmed sequences involved. This leap forward comes amid Tesla’s broader push into humanoid robotics, aiming to deploy these machines for factory work, household chores, and beyond. Advancements in AI Training and Hardware Engineers at Tesla have reportedly trained Optimus using end-to-end neural networks, similar to those in the company’s autonomous driving systems, allowing the robot to process visual data and translate it into physical responses in real time. According to a report from Digital Trends, the display is impressive for an adult-sized robot, showcasing speed, balance, and accuracy that rival human capabilities. The publication notes that while earlier videos featured Optimus dancing or folding laundry, this kung fu routine demonstrates improved joint flexibility and coordination, potentially paving the way for applications in security or entertainment. Comparisons to competitors like Boston Dynamics’ Atlas or Figure’s humanoid bots reveal Tesla’s unique edge: scalability. Musk has projected low-volume production for internal use by next year, with high-volume output targeted for 2026. Insights from Interesting Engineering highlight how Optimus’s kung fu training refines its motor skills, drawing from simulation-based learning where virtual models endure millions of iterations before real-world deployment. Implications for Industry and Ethical Considerations Industry insiders see this as a milestone in humanoid robotics, potentially disrupting labor markets in manufacturing and service sectors. Tesla’s approach leverages its vast data from electric vehicles to accelerate robot development, with Optimus priced under $30,000 per unit for mass appeal. A piece in The Times of India describes the robot flawlessly replicating kung fu alongside a trainer, emphasizing AI’s role in achieving human-like balance without engineer oversight. However, such capabilities raise questions about safety and job displacement. If robots can master martial arts, their potential in hazardous environments—like disaster response—becomes evident, but so do concerns over misuse. Musk has addressed this by stressing ethical AI frameworks, though skeptics on platforms like X argue for more transparency in training data. Future Trajectories and Competitive Pressures Looking ahead, Tesla plans to integrate Optimus with its Grok AI for conversational abilities, expanding beyond physical feats. Posts on X from robotics enthusiasts, including those analyzing the video’s frame-by-frame mechanics, suggest this kung fu demo is a precursor to more autonomous behaviors, like adaptive learning in unstructured settings. Rivals are not idle; companies like Unitree have showcased similar agile robots, but Tesla’s ecosystem integration gives it an advantage. As reported by TechEBlog, Optimus V3’s moves start with a fist touch, evolving into sparring that blurs lines between machine and human. For industry leaders, this signals a new era where robots don’t just assist but emulate human prowess, promising efficiencies while challenging us to redefine work in an automated world. In wrapping up, Tesla’s kung fu-capable Optimus isn’t merely entertainment—it’s a harbinger of robotics’ mainstream integration, driven by relentless innovation and AI prowess. Subscribe for Updates Help us improve our content by reporting any issues you find. Get the free daily newsletter read by decision makers Get our media kit Deliver your marketing message directly to decision makers.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reports volume 15, Article number: 32506 (2025) Cite this article 4434 Accesses Metrics details In modern industrial and laboratory environments, robotic arms often operate in complex, cluttered spaces. Ensuring reliable obstacle avoidance and efficient motion planning is therefore essential for safe performance. Motivated by the shortcomings of traditional path planning methods and the growing demand for intelligent automation, we propose a novel reinforcement learning framework that combines a modified artificial potential field (APF) method with the Deep Deterministic Policy Gradient algorithm. Our model is formulated in a continuous environment, which more accurately reflects real-world conditions compared to discrete models. This approach directly addresses the common local optimum issues of conventional APF, enabling the robot arm to navigate complex three-dimensional spaces, optimize its end-effector trajectory, and ensure full-body collision avoidance. Our main contributions include the integration of reinforcement learning factors into the APF framework and the design of a tailored reward mechanism with a compensation term to correct for suboptimal motion directions. This design not only mitigates the inherent limitations of APF in environments with closely spaced obstacles, but also improves performance in both simple and complex scenarios. Extensive experiments show that our method achieves safe and efficient obstacle avoidance with fewer steps and lower energy consumption compared to baseline models, including a TD3-based variant. These results clearly demonstrate the significant potential of our approach to advance robot motion planning in practical applications. Multidegree-of-freedom robotic arms are an integral part of today’s industrial automation, manufacturing, and service applications. As these systems are deployed in increasingly complex and dynamic environments, achieving robust motion planning and reliable obstacle avoidance has become a critical challenge. In many practical scenarios, the robot must navigate through cluttered spaces and avoid collisions while optimizing its trajectory for efficiency and safety. Efficient motion planning is essential for safely performing tasks in environments that are not only cluttered but also subject to dynamic changes. Conventional planning methods often struggle with adapting to unpredictable obstacles or high-dimensional configuration spaces. Traditional methods can be broadly categorized into searching-based, bionic and evolutionary, sampling-based, and gradient-based techniques. Searching-based algorithms, such as A*1, D*2, Dijkstra3, and simulated annealing4, are efficient in static, small-scale environments. However, they become impractical in large-scale, dynamic scenarios. Bionic methods like Genetic Algorithms (GA)5, Particle Swarm Optimization (PSO)6, Ant Colony Optimization (ACO)7, and fuzzy logic8 can handle more complex environments but often suffer from slow convergence and local optima. Sampling-based approaches, such as Rapidly-Exploring Random Trees (RRT)9 and Probabilistic Roadmaps (PRM), can navigate intricate spaces but at the cost of computational complexity. Gradient-based methods, especially the artificial potential field (APF) method10, offer intuitive and computationally efficient solutions by modeling the workspace as a potential field. APF methods attract the end-effector toward the goal and repel it from obstacles, yet they are inherently vulnerable to local minima problems where the robot becomes trapped due to balanced attractive and repulsive forces11,12. To address these limitations, hybrid strategies have been proposed. Integrating methods such as RRT with APF9, ACO with APF13, and Dynamic Window Approach (DWA) with GA14 have been explored. Researchers have also enhanced APF directly by modifying repulsive field functions or introducing auxiliary constructs. Cao15 proposed a velocity potential field (VPF) using robot velocity instead of distance, Flacco et al.16 leveraged repulsive vectors for collision avoidance, and Zhang et al.17 adjusted repulsive force functions to mitigate local minima. In parallel, reinforcement learning (RL) methods, particularly Deep Deterministic Policy Gradient (DDPG), have emerged as robust tools for continuous control problems18. Early RL algorithms, such as Deep Q-Networks (DQNs)19, DDPG18, and SARSA20, enabled agents to learn optimal policies by interacting with their environment. Recent advancements have further expanded these techniques. Chen21 utilized DDPG, TD3, and SAC effectively in robotic tasks, while Imtiaz22 demonstrated the versatility of PPO in fruit-picking applications. Improvements such as the multi-critic mechanism by Sze23 and refined reward functions by Li et al.24 have enhanced policy evaluation and APF optimization, respectively. Hybrid approaches combining RL with traditional methods, like RRT with TD325, DWA with Q-learning26, and PRM with RL27, further illustrate the complementary potential of these strategies. Despite these advancements, critical challenges persist, notably the local minima issue in APF methods and the sensitivity of RL algorithms to reward function design. The APF method’s vulnerability to local minima arises when nearby obstacles counterbalance the attractive goal force, trapping the robot prematurely. Conversely, DDPG and similar RL methods can learn complex continuous action policies but often struggle with limited or misleading rewards in complex environments. Our proposed approach integrates a modified APF mechanism within the DDPG framework, combining APF s structural guidance to alleviate local minima and DDPG s adaptive learning capabilities. Specifically, we enhance the reward signal with APF-derived attractive and repulsive components. Our state space includes both joint angles and end-effector positions, and actions comprise fine-grained joint rotations. This hybrid strategy enables efficient obstacle avoidance and optimized motion planning in a fully continuous state and action space, in contrast to the discrete formulations used in some prior work. While some prior approaches combine APF with RL methods such as Q-learning or PPO28,29, they typically rely on discretized state spaces, task-specific constraints, or lack the fine-grained control needed for articulated robotic arms. Others focus primarily on mobile robots, where the configuration space is of lower dimensionality and local minima are easier to mitigate. In contrast, our approach is explicitly designed for continuous 6-DOF robotic arms operating in 3D space, where both configuration complexity and the risk of local minima are substantially higher. Moreover, our APF-informed reward function is designed to remain informative throughout training, facilitating faster convergence and more stable behavior across environments of varying complexity. The contributions of our work are threefold: First, we present a novel APF-DDPG hybrid framework that combines the advantages of traditional and learning-based methods in the continuous setting. Second, we propose an improved reward mechanism explicitly designed to mitigate local minima. Finally, we validate our framework through extensive experiments across diverse environments, demonstrating significant improvements in convergence, solution quality, and collision avoidance compared to baseline models. Our findings provide robust solutions for real-world robotic arm motion planning. In this work, we employ a standard 6-degree-of-freedom industrial manipulator30 as our experimental platform. The kinematic model of the robot is derived using the standard Denavit–Hartenberg (DH) convention. According to this convention, each joint is assigned a coordinate frame, enabling the definition of spatial transformations between consecutive joints. Using these DH parameters, we calculate the forward kinematics to determine the robot configuration. Each transformation matrix \({}^{i-1}\mathbf{T}{i}\) from joint \(i-1\) to joint i is defined as The complete forward kinematic transformation from the base frame to the end-effector frame is obtained by multiplying these matrices sequentially: From \({}^{0}\mathbf{T}_{6}\), the end-effector position (x, y, z) is extracted and used for collision detection and distance computations in the obstacle avoidance algorithm. By incorporating forward kinematics directly into our methodology, we simplify computational requirements while ensuring accurate spatial data for our reinforcement learning framework. In this work, we propose an obstacle avoidance approach that integrates APF with DDPG reinforcement learning31. This integration addresses critical limitations of each method individually. APF methods often encounter local minima problems, particularly when attractive and repulsive forces oppose each other32. Pure DDPG methods, in contrast, typically require extensive exploration and lack direct spatial information, potentially slowing convergence and complicating navigation. The combined APF-DDPG framework thus leverages structured spatial information from APF and the robust exploratory learning ability of DDPG, facilitating efficient and reliable obstacle avoidance in continuous state-action scenarios. We define the obstacle avoidance problem as a Markov Decision Process (MDP), described by a state space \(\mathscr {S}\), action space \(\mathscr {A}\), and a reward function R(s, a). The continuous state vector \(s \in \mathbb {R}^{9}\) comprises the robot s six joint angles \((\theta _1,\dots ,\theta _6)\) and the end-effector’s Cartesian coordinates (x, y, z) computed from forward kinematics. The action vector \(a \in \mathbb {R}^{6}\) specifies incremental joint angle adjustments within the interval of \([-0.5^\circ , 0.5^\circ ]\) per timestep, allowing smooth and controlled movements. The APF-DDPG framework consists of actor and critic neural networks, their respective target networks, and an experience replay buffer for stable training, as illustrated in Fig. 1. At each timestep, the actor network outputs continuous incremental actions given the current state, which are then executed by the robot. The critic network evaluates state-action pairs by estimating the expected cumulative rewards (Q-values). Integrated APF-DDPG framework for continuous obstacle avoidance. The main networks (actor and critic) use spatial information from APF directions to guide policy updates. Target networks stabilize training. Experiences \((s,a,r,s')\) are sampled from the replay buffer to decorrelate training updates. The networks are trained using mini-batches sampled from the replay buffer, where each stored experience tuple \((s,a,r,s')\) captures interactions with the environment. To ensure stable training and smooth convergence, we follow32 and employ soft target network updates, defined by where \(\theta '\) are target network parameters, \(\theta\) are main network parameters, and \(\tau\) being the smoothing coefficient. We integrate APF into our DDPG framework to explicitly inform the actor network of desirable spatial directions. To do this, we first recall the standard artificial potential field definition10 where \(q\) is the end-effector position, \(q_{\textrm{goal}}\) the goal position, \(\rho (q)\) the distance to the nearest obstacle, and \(\rho _0\) its influence radius. Forces arise as the negative gradients of these potentials: The net direction of the APF is then \(F_\textrm{net} = F_a + F_r\). However, when \(F_a\) and \(F_r\) directly oppose each other, the robot can become trapped in a local minimum (\(\Vert F_\textrm{net}\Vert \approx 0\)). To systematically escape such traps, drawing inspiration from the classical BUG principle33, we add a small orthogonal perturbation. Specifically, we define Because \(F_\perp\) is normal to the plane spanned by \(F_a\) and \(F_r\), this small perturbation guarantees movement off any planar saddle without significantly diverting the primary APF direction. The adjusted force \(F_{\textrm{adjusted}}\) is then provided to the actor network at each time step, biasing policy updates toward collision-free, goal-directed motion. The compensation mechanism is schematically illustrated in Fig. 2. Illustration of perpendicular compensation in APF direction. When repulsive and attractive forces nearly oppose each other, a perpendicular correction is introduced, steering the robot away from local minima. The reward function is carefully designed to balance obstacle avoidance and goal-oriented behavior by combining two components: proximity to the goal and directional alignment with APF guidance. Formally, we write where \(\textrm{distanceGE}\) is the Euclidean distance between the end-effector and the goal in Cartesian space, explicitly encouraging movements toward the goal. The term \(\cos (\sigma )\in [-1,1]\) measures the alignment between the executed displacement vector \(\overrightarrow{\textrm{EE}}\) and the APF-suggested vector \(\overrightarrow{\textrm{APF}}\), computed as We choose \(\mu >1\) so that \(\mu (\cos \sigma -1)\le 0\) with equality only when \(\cos \sigma =1\), ensuring every deviation from perfect alignment incurs a negative reward and thereby shaping the agent to follow efficient, APF-consistent motions. To incorporate safety constraints we distinguish collision scenarios by using separate distance-weighting parameters, defining where \(\lambda _{c}>\lambda _{nc}\) is chosen based on the ratio between the workspace size and the robot arm length so that the distance penalty scales appropriately with the environment s spatial dimensions relative to the manipulator. We allow collisions during training rather than terminating episodes to promote extensive exploration of the state action space and to enable the agent to learn both avoidance and recovery strategies. Unlike pure APF methods that stall in local minima, our formulation maintains a negative distance term \(R_{\textrm{dist}} = -\lambda \,\textrm{distanceGE}\) until the goal is reached. We apply a per-step penalty to prevent hesitation or oscillation within shallow basins. We allow non-terminating collisions with a larger penalty \(\lambda _{c}\), encouraging risky detours and subsequent recovery to uncover escape pathways. We use the alignment term \(R_{\textrm{align}} = \mu (\cos \sigma - 1)\le 0\) to penalize moves that conflict with APF guidance. As a result, the agent converges on efficient, collision-free trajectories. Due to the absence of specialized datasets for robotic arm path planning and obstacle avoidance, we developed a series of custom environments to simulate diverse real-world scenarios. Each obstacle within these environments is assigned a region characterized by a specific repulsion scaling factor, resulting in localized repulsive fields that directly affect the agent s trajectory planning. All environments share a common workspace dimension of \(10 \times 10 \times 10\) units. Spherical obstacles are placed strategically and vary in radius from \(0.2\) to \(0.5\) units, while the robotic arm itself, when fully extended, approximates a cylinder of length \(10\) units with a radius of \(0.1\) units. We designed two distinct categories of task environments to rigorously evaluate the proposed approach: Simple task environments These environments include fewer than ten spherical obstacles strategically arranged to create deliberate local optima traps, as depicted in Fig. 3a. These scenarios provide baseline conditions to assess fundamental obstacle avoidance capabilities. Left: Example of a simple task environment containing fewer than ten spherical obstacles positioned to create local optima traps. Right: Example of a complex task environment, illustrating increased obstacle density and strategic placement to enhance task difficulty. (These images were made with Unity-2022.3.18f1, https://unity.com/de). Complex task environments These scenarios incorporate a higher number and strategic complexity of obstacles, significantly increasing the likelihood of collisions and entrapment in local optima. Obstacles are placed near the initial position to hinder initial movements and along intended trajectories to elevate the complexity of the planning problem. Figure 3b illustrates typical complex environments. An additional challenging scenario includes environments featuring cuboid wall obstacles (0.5 units thickness, 5 units width, and 10 units height), as shown in Fig. 4. The initial placement (Fig. 4a), along with the front (Fig. 4b) and side (Fig. 4c) views of the final state, emphasize how the dimensions of the wall impose substantial restrictions on the agent’s movement, covering approximately 30–40% of the width of the workspace and 50% height, providing a strict test for obstacle avoidance capabilities. A complex task environment featuring cuboid wall obstacles, significantly restricting movement space compared to spherical obstacles. (These images were made with Unity-2022.3.18f1. The experimental setup comprises a total of 30 simple and 30 complex environments. Additionally, multi-goal trajectory planning tasks were designed, where the final state of one iteration serves as the initial state of the next. Task completion is achieved when the end-effector reaches the goal within a 0.1-unit tolerance, optimizing for computational efficiency and practical accuracy. For intuitive visualization and precise control of the robotic agent, Unity, a real-time 3D development platform, is employed. Control scripts are implemented using C# to directly interact with the Unity simulation environment. Experiments were conducted on a computing system with an 11th Gen Intel(R) Core(TM) i5-11320H processor and NVIDIA GeForce MX450 GPU. The reinforcement learning framework was implemented using PyTorch 2.0.1. Detailed hardware and software configurations are summarized in Table 1. The choice of baseline methods was motivated by their contrasting characteristics, strengths, and limitations relevant to robotic arm path planning. To fully evaluate the effectiveness of the proposed DDPG-APF approach, we compare with the following baselines: A* Search (Non-RL) A classical best-first search algorithm operating in a discretized joint-action space (rotations of − 1 , 0 , + 1 per joint). A* is run without a step limit, relying on an admissible and consistent heuristic to guarantee optimality in discrete settings34. This method serves as a non-learning baseline, highlighting the challenges of discretization and potential local-optima loops in our custom environments. Pure-DDPG A standard Deep Deterministic Policy Gradient model without Artificial Potential Field guidance, using only collision penalties and distance-to-goal in its reward function. This baseline isolates the benefit of APF integration and demonstrates the intrinsic performance limitations and convergence challenges faced by reinforcement learning without heuristic guidance. TD3-APF Twin Delayed DDPG with the same APF-based reward as our DDPG-APF agent. TD3 employs dual critics and delayed actor updates to address DDPG’s overestimation bias35, enabling a direct comparison under identical reward and network settings to assess whether these algorithmic improvements provide tangible benefits in complex, continuous task spaces. All reinforcement learning methods share identical network architectures and training regimens, ensuring a fair evaluation of feasibility, optimality, computational efficiency, and convergence behavior. Together, these methods provide a comprehensive performance context, ranging from classical planning paradigms to modern deep reinforcement learning techniques. We evaluated the performance of four distinct algorithms: DDPG-APF, Pure-DDPG, TD3-APF, and the classical A* search algorithm. This evaluation was conducted across tasks of varying complexity, focusing specifically on path optimality, solution feasibility, computational efficiency, and learning dynamics. All RL algorithms were trained under identical hyperparameters, explicitly detailed in Table 2. Initially, we analyzed training dynamics and convergence characteristics. The progression of best solutions, quantified by the number of steps required to reach the goal, revealed clear distinctions among the evaluated methods. Initially, all algorithms maintained a step count at the maximum threshold of 200 steps, indicating the absence of early feasible solutions. Upon discovering feasible solutions, the DDPG-APF algorithm exhibited notably faster convergence, consistently achieving shorter paths at an earlier stage, as demonstrated in Fig. 5. Feasible solution evaluation illustrating episodes required for finding the first feasible path. DDPG-APF identifies feasible paths sooner and more consistently than other methods, reflecting superior stability and efficiency. While the episode count to first feasible solution quantifies when each agent begins to find successful paths, analyzing the evolution of the average reward provides deeper insights into how effectively each policy learns over time. Specifically, tracking rewards captures both the stability of learning dynamics and the efficiency with which algorithms balance exploration, collision avoidance, and path-length minimization (see Fig. 6). Figure 6a illustrates the overall average reward curves across all task environments. Here, DDPG-APF consistently achieves a higher average reward compared to Pure-DDPG and TD3-APF, signaling more efficient policy improvements and a superior exploration-exploitation trade-off. In contrast, Pure-DDPG demonstrates a smoother but slower progression, reflecting a stable yet less efficient learning pattern, whereas TD3-APF displays noticeable fluctuations, likely resulting from increased complexity associated with its dual-critic network structure. Comparison of the presented reward functions: (a) overall model performance across all tasks; (b) sensitivity to reward-function design. We further assessed the sensitivity of the RL algorithms to variations in reward function design. Two distinct reward functions were tested: the original APF method without modifications and our proposed compensated APF reward function. Reward curves averaged across all tasks, displayed in Fig. 6b, demonstrated that DDPG-APF maintained stable performance regardless of reward function variations. Conversely, TD3-APF exhibited greater sensitivity, likely due to additional complexity in its dual-network architecture aimed at mitigating value function overestimation. In comparing RL methods to classical non-learning approaches, we evaluated the A* search algorithm, which operates using discrete action spaces and heuristic search. A comprehensive summary of key performance metrics across all 60 scenarios is provided in Table 3. Despite having unlimited search steps per episode, A* exhibited significantly lower success rates compared to the RL-based methods. Specifically, DDPG-APF demonstrated superior performance in average steps, success rate, and episodes required to achieve first success. Moreover, a dispersion analysis shows that DDPG-APF s variability in average steps is roughly half that of Pure-DDPG and one-third that of TD3-APF, and its variability in episodes to first success is about 45% lower than Pure-DDPG and over 65% lower than TD3-APF clear evidence of its consistency across diverse scenarios. The results highlight the advantages of continuous action adjustments offered by DDPG-APF, particularly in scenarios sensitive to local optima. Further evaluation against other RL metrics, such as time per episode and stability of solution improvement, reinforces the robustness of the DDPG-APF method. DDPG-APF consistently outperformed Pure-DDPG and TD3-APF across all metrics, particularly in computational efficiency (Time/Episode), indicating faster convergence and more effective exploration strategies. Notably, DDPG-APF reduces time-per-episode variability by approximately 25% relative to TD3-APF, underscoring its reliable runtime behavior. TD3-APF, while theoretically robust, showed notably higher variability and longer episodes to achieve first success, highlighting challenges associated with its dual-critic architecture. Computational efficiency was further analyzed by measuring average runtime per episode across tasks with different complexity levels. Table 4 summarizes these results, showing DDPG-APF consistently offering computational advantages over TD3-APF and Pure-DDPG. These runtime trends align well with our task complexity classification. Average episode runtime increases steadily from simple tasks through complex tasks without wall to complex tasks with wall. The additional runtime in scenarios containing walls reflects the greater number of collision checks and potential field updates required to navigate planar obstacles. This monotonic increase in computational load confirms that our categorization of simple and hard tasks is reasonable and that wall environments rightly belong in the hard category. To identify the precise contributors to these runtimes we divide each episode into agent runtime and environment preparation runtime. Agent runtime comprises action selection at every step and network updating every fifty steps and is dominated by reinforcement learning computations. Environment preparation updates the environment state around the agent and is driven primarily by artificial potential field calculations. For the DDPG-APF agent action selection requires around 0.5 ms network updating approximately 240 ms and environment preparation about 2 ms. For the TD3-APF agent action selection requires around 0.7 ms network updating approximately 280 ms and environment preparation about 2 ms. Across all analyzed metrics, DDPG-APF consistently outperforms both RL baselines and the classical A* method, validating the benefit of APF-guided reward shaping in continuous control settings. The APF-DDPG framework illustrates how combining structural guidance with adaptive learning can yield both stability and efficiency in robotic motion planning. Our experiments highlight clear benefits over classical approaches and pure reinforcement learning, but they also expose new compromises that arise from this hybrid design. In the following, we reflect on these strengths and weaknesses, emphasizing the trade-offs between guidance and exploration, efficiency and flexibility, as well as simulation and real-world applicability. Using a continuous state-action formulation provides clear benefits over discretized or inverse kinematics-based methods. Continuous incremental actions allowed the agent to adapt flexibly to dynamic obstacle configurations, enabling responsive trajectory adjustments. At the same time, the use of forward kinematics reduced computational complexity by avoiding costly inverse kinematics calculations, resulting in shorter episode runtimes compared to baseline methods. A drawback is that this formulation inherently enlarges the search domain, requiring careful reward shaping and parameter tuning to maintain stability; without strong guidance, training in such a continuous space may become less robust. The framework demonstrated improved learning efficiency, with faster convergence and more stable reward trajectories. The dense reward function provided continuous and informative feedback, facilitating effective optimization and reducing the number of collision trials. On the other hand, this reliance on a dense reward structure represents a limitation: if such shaping is not available, or must be inferred from noisy real-world signals, efficiency and stability may degrade substantially. Integrating a modified APF within the DDPG framework shaped the reward structure into a smoother potential field that continuously guided the agent toward the goal while repelling it from obstacles. This reduced fluctuations, stabilized learning, and biased exploration toward safer paths, improving sample efficiency and runtime compared to Pure-DDPG. At the same time, this integration introduced important trade-offs. The strong directional bias of APF may suppress the discovery of unconventional but potentially more efficient trajectories, and the perpendicular compensation term, while effective in our experiments, could in principle induce oscillatory motion in narrow passages. Moreover, the continuous computation of potential field forces and collision checks adds a runtime overhead of approximately 2 ms per step. Our experiments indicate that this cost is outweighed by fewer detours, reduced collisions, and faster convergence, yet its impact under noisy sensing or dynamic obstacles remains an open question. Together, these findings highlight both the stabilizing benefits and the potential risks of APF guidance. Our experiments revealed two concrete shortcomings of the current implementation. First, the framework lacks adaptability in adjusting step limits to different task complexities, which led to degraded performance in harder scenarios. Second, the method struggled with smooth transitions in multi-goal trajectory planning, limiting its effectiveness in sequential manipulation tasks. These weaknesses point to the need for adaptive step management and hierarchical or goal-decomposition strategies in future work. The dataset and evaluation design impose further limitations. All environments were manually created to challenge the robot arm, which restricts generalizability and prevents a definitive claim that APF-DDPG consistently outperforms other agents. The absence of standardized benchmarks for manipulator path planning further complicates comparisons across studies. Moreover, although oscillatory behavior was not observed in our fewer than 100 task instances, it cannot be excluded on larger or more uniform benchmarks. Parameter tuning across algorithms is another source of uncertainty: TD3, for example, did not achieve the expected improvements over DDPG, possibly because its double actor-critic structure was less advantageous in our task setup. These findings suggest that notions such as overestimation in reinforcement learning should be reconsidered in context, as optimistic Q-value estimates in DDPG may sometimes aid exploration rather than hinder it. The APF-DDPG framework is particularly suited to industrial scenarios where manipulators must operate safely in highly cluttered but largely static workcells. Examples include dynamic pick-and-place in warehouses or laboratory automation tasks, where dense static obstacles constrain free exploration and safety demands are high. In such settings, the spatial guidance of APF can provide reliable safety margins, while reinforcement learning enables adaptability to variations in object placement. In contrast, long-range navigation or human robot collaboration with highly dynamic obstacles may be less suited, since strong APF bias could hinder the discovery of unconventional solutions. Short- to mid-range manipulation in dense, safety-critical environments therefore appears to be the most promising niche for our approach. Finally, the transferability of our findings from simulation to real-world deployment remains a challenge. The modeling assumptions made in our setup are fragile under realistic conditions. For instance, the UR5 model neglects actuator dynamics, backlash, and latency, which influence fine-grained incremental control. Likewise, APF forces were computed under idealized, noise-free conditions, whereas real-world sensing inevitably introduces uncertainty in obstacle localization. The obstacles themselves were represented by simple geometric primitives, in contrast to the irregular and textured shapes encountered in practice. These simplifications suggest that APF-DDPG may be particularly sensitive to sensor noise and geometric mismatches. Future work should therefore investigate robustness under imperfect perception and more realistic robot models to narrow the sim-to-real gap. In this paper, we presented a reinforcement learning framework integrating a modified APF method with DDPG for robotic arm motion planning in continuous 3D environments. Our approach addresses the local optimum issues inherent in traditional APF methods through a tailored reward mechanism. Experimental results demonstrated that our method can find feasible solutions in fewer episodes and typically achieves more optimal paths compared to baseline models. Although APF introduces extra computation per step, in practice this overhead is outweighed by more efficient trajectories, leading to faster overall execution. The findings suggest that APF-based guidance not only accelerates convergence but also improves computational efficiency, making it suitable for complex obstacle avoidance tasks. However, the method’s current limitations, particularly the inability to automatically adjust step limits for varying complexities and difficulties in smoothly handling multi-goal trajectory planning, highlight important areas for further improvement. 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Reprints and permissions Schneider, LS., Peng, J. & Maier, A. Robot movement planning for obstacle avoidance using reinforcement learning. Sci Rep 15, 32506 (2025). https://doi.org/10.1038/s41598-025-17740-5 Download citation Received: 12 May 2025 Accepted: 26 August 2025 Published: 12 September 2025 Version of record: 12 September 2025 DOI: https://doi.org/10.1038/s41598-025-17740-5 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Advertisement Scientific Reports (Sci Rep) ISSN 2045-2322 (online) © 2026 Springer Nature Limited Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.