Description: Глава компании Tesla Илон Маск в интервью на конференции Abundance Summit 11 марта 2026 года сообщил о сроках начала выпуска человекоподобного робота Optimus 3. По его словам, производство устройства намечено на лето текущего года, при этом первые партии будут немногочисленны и станут наращиваться по классической для производств S-образной кривой. Маск охарактеризовал новинку как наиболее совершенного
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Глава компании Tesla Илон Маск в интервью на конференции Abundance Summit 11 марта 2026 года сообщил о сроках начала выпуска человекоподобного робота Optimus 3. По его словам, производство устройства намечено на лето текущего года, при этом первые партии будут немногочисленны и станут наращиваться по классической для производств S-образной кривой. Маск охарактеризовал новинку как наиболее совершенного робота в мире, не имеющего аналогов среди существующих разработок. Optimus 3 станет третьей итерацией человекоподобного робота, разрабатываемого Tesla с 2022 года. Предыдущая версия Gen 2 демонстрировала возможности ходьбы со скоростью 0,6 метра в секунду, выполняла операции по сортировке элементов питания 4680 и оснащалась 22 степенями свободы в кистях рук, что позволяло манипулировать хрупкими предметами. Новая модель, согласно заявлениям Маска, получит качественно иной уровень искусственного интеллекта, позволяющий обучаться новым действиям посредством наблюдения за человеком, без необходимости программирования отдельных движений. В основе системы лежит фирменный бортовой компьютер FSD (Full Self-Driving) версии 15, адаптированный для задач встраиваемого интеллекта. Производственная стратегия Tesla в отношении роботов предусматривает поэтапное масштабирование. Первая сборочная линия мощностью до одного миллиона единиц в год будет развернута на заводе компании во Фримонте (Калифорния). Именно на этой площадке ожидается выпуск первых экземпляров Optimus 3. Одновременно компания готовит инфраструктуру для гораздо более крупного производства в Техасе: на территории гигафабрики Giga Texas уже ведутся подготовительные земляные работы под строительство отдельного предприятия, проектная мощность которого должна составить 10 миллионов роботов в год. Маск охарактеризовал будущие темпы развертывания как самые быстрые в истории для сложных промышленных изделий. Выход на действительно крупные объемы выпуска связывают уже со следующим поколением — Optimus 4. По словам Маска, его разработка должна завершиться в 2027 году, и именно эта версия будет производиться преимущественно в Техасе в значительно больших количествах. Глава Tesla также подтвердил намерение обновлять модельный ряд роботов ежегодно, добиваясь быстрого технического прогресса. Одним из ключевых барьеров для массового распространения Optimus остается стоимость. По оценкам аналитиков, благодаря использованию собственных приводов, вертикальной интеграции и эффекту масштаба, производственные расходы на одного робота могут быть снижены до 20 тысяч долларов. Это сопоставимо с ценой недорогого автомобиля и открывает перспективы использования таких машин не только в промышленности, но и в домашнем хозяйстве. Критически важной особенностью Optimus 3 называют полностью независимую цепочку поставок комплектующих. Tesla сознательно отказалась от использования готовых решений, доступных на рынке промышленной робототехники, и спроектировала все ключевые узлы — актюаторы, сенсоры, элементы питания и вычислительные модули — самостоятельно. Такой подход, основанный на «первых принципах», неизбежно удлиняет этап отладки производства и выхода на плановую мощность, но, по мнению руководства компании, позволит сохранить полный контроль над технологией в долгосрочной перспективе. Параллельно с физической версией робота, Tesla совместно с принадлежащей Маску компанией xAI разрабатывает программный продукт под названием Digital Optimus. Эта система, как пояснил предприниматель в социальной сети X, представляет собой «искусственный интеллект для офисного работника», способный выполнять рутинные операции за компьютером, наблюдая за действиями человека и повторяя их. Таким образом, стратегия Tesla в области автоматизации охватывает как физический труд, так и сферу интеллектуальной деятельности. Конкуренция на рынке человекоподобных роботов обостряется. Помимо китайских производителей, активность проявляют Boston Dynamics с коммерческой версией Atlas, Figure AI при поддержке Microsoft и OpenAI, а также Google DeepMind, предлагающий сторонним производителям базовую модель Gemini Robotics. На этом фоне успех Optimus будет зависеть не только от технического совершенства аппарата, но и от способности Tesla организовать его рентабельное массовое производство в ранее недостижимых для этой отрасли масштабах. Маск уже называл Китай единственным серьезным конкурентом в данной сфере. Глава компании Tesla Илон Маск в интервью на конференции Abundance Summit 11 марта 2026 года сообщил о сроках начала выпуска человекоподобного робота Optimus 3. По его словам, производство устройства намечено на лето текущего года, при этом первые партии будут немногочисленны и станут наращиваться по классической для производств S-образной кривой. Маск охарактеризовал новинку как наиболее совершенного робота в мире, не имеющего аналогов среди существующих разработок. Δ Для пресс-релизов и писем: news@upweek.ru
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Tesla показала человекоподобного робота Optimus. Что он умеет?
Description: Генеральный директор Tesla показал прототип человекоподобного робота Optimus, пишет The Guardian. Он вышел на сцену и помахал рукой сидящей публике. Как отметил Илон Маск, это первая такая самостоятельная и автономная прогулка аппарата. Далее был продемонстрирован небольшой ролик с другими
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Генеральный директор Tesla показал прототип человекоподобного робота Optimus, пишет The Guardian. Он вышел на сцену и помахал рукой сидящей публике. Как отметил Илон Маск, это первая такая самостоятельная и автономная прогулка аппарата. Далее был продемонстрирован небольшой ролик с другими возможностями робота: на нем он носит коробки, поливает растения и перемещает металлические прутья на заводе автопроизводителя. Optimus использует те же сенсоры, камеры и компьютер, которые отвечают за функцию автопилота в электромобилях Tesla. Он также самообучается. Встроенной батареи роботу хватает на сутки. Весит от 73 кг. Tesla Bot coming out and dancing @elonmuskpic.twitter.com/TKT1lSGyqa — Tesla Owners Silicon Valley (@teslaownersSV) October 1, 2022 — Главная цель — создание робота, который может заменить собой человеческую рабочую силу и использоваться на производстве и в быту, — заявил глава Tesla. Наш канал в Telegram. Присоединяйтесь! Есть о чем рассказать? Пишите в наш телеграм-бот. Это анонимно и быстро
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Cette start-up valorisée à 14 milliards de dollars développe un …
Description: Et si tous les robots pouvaient fonctionner avec le même cerveau ? SoftBank veut le découvrir. Le 14 janvier, la startup de robotique Skild AI a levé 1,4 mil...
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Yahboom DOGZILLA-Lite: The First AI LLM Embodied Intelligence Robot Dog …
DOGZILLA-Lite is presented as the world’s first educational robot dog that seamlessly integrates multimodal large language models (LLM) with embodied intelligence. Built around a Raspberry Pi module, this advanced platform supports multiple AI visual functions, including face detection and sophisticated object recognition. DOGZILLA-Lite is more than just a walking robot; it is a true AI partner capable of understanding images, voices, environmental cues, and making complex autonomous decisions. The platform supports Robot Arm expansion, enabling the extension of a 3DOF robotic arm for autonomous object grasping and handling. It comes pre-programmed with a graphical user interface (GUI) system that includes built-in AI vision and voice programs. These programs unlock numerous exciting functions such as 3D object recognition, color identification, face and emotion recognition, and motion detection, providing endless possibilities for creative and educational projects. It should be noted that the robotic arm is designed to grasp standard EVA cubes and balls. Users benefit from Multiple Control Methods and real-time visual feedback. DOGZILLA-Lite can be easily controlled via the XGO APP and PC software, compatible with both Android and iOS devices. The robot dog can transmit real-time video images directly to the application, providing the user with an immersive first-person perspective control experience. The robot features highly advanced Gait Planning and free adjustment capabilities. DOGZILLA-Lite integrates inverse kinematics algorithms to accurately control the ground contact time, lift time, and lift height of each leg. Users can easily adjust these parameters to achieve different complex gaits. Detailed inverse kinematics analysis and the source code for these functions are provided for deeper learning. DOGZILLA-Lite is positioned not just as a toy, but as a ticket to the future of technology. Students can use it to understand core AI principles, developers and geeks can use it to create and test autonomous driving algorithms, and families can enjoy it as an interactive technology partner. Yahboom provides extensive technical support, including open-source data code for AI visual interaction, Open CV, and AI LLM development.
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Naver to develop Arabic-based LLM, expand AI cooperation with Saudi …
Description: SEOUL, Sept. 13 (Yonhap) -- Naver Corp., the operator of South Korea's largest intern...
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All Headlines North Korea Sports Top News Most Viewed Korean Newspaper Headlines Today in Korean History Yonhap News Summary Editorials from Korean Dailies URL is copied. SEOUL, Sept. 13 (Yonhap) -- Naver Corp., the operator of South Korea's largest internet platform, has signed an initial agreement with Saudi Arabia's artificial intelligence (AI) agency to jointly develop an Arabic language-based large language model (LLM), company officials said Friday. During the Global AI Summit hosted by the Saudi Data & AI Authority (SDAIA) in Saudi Arabia's capital of Riyadh earlier this week, Naver and the SDAIA signed the memorandum of understanding (MOU) to cooperate in various sectors, including AI, cloud computing, data centers and robots, according to the officials. Under the MOU, the two sides plan to jointly develop an Arabic LLM, and technology solutions and services in the fields. SDAIA has been leading the Middle Eastern nation's ambitious plan of creating a technology-driven economy by 2030. Last year, Naver also struck a deal with the Saudi Arabian government to create a digital twin platform for Riyadh and four other Saudi cities. Naver Corp.'s executives are seen attending the Global AI Summit in Riyadh, hosted by the Saudi Data & AI Authority, in this photo provided by the Korean company on Sept. 12, 2024. (PHOTO NOT FOR SALE) (Yonhap) nyway@yna.co.kr(END) All News National North Korea Economy/Finance Biz Culture/K-pop Sports Images Videos Top News Most Viewed Korean Newspaper Headlines Today in Korean History Yonhap News Summary Editorials from Korean Dailies Korea in Brief Useful Links Weather Advertise with Yonhap News Agency
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Robots humanoïdes : la Chine montre en spectacle sa domination …
Description: Le Gala du Nouvel An lunaire a mis en scène des dizaines de robots humanoïdes capables de prouesses martiales. La Chine confirme ainsi son avance stratégique sur l'IA incarnée par des robots.
Description: David Reger explains how Neura's 4NE1 humanoid robots address a 101 million worker shortage by 2030. AWS partnership scales physical AI for manufacturing.
Description: Explore a diverse array of events in IN. Find & compare, Reviews, Ratings, Timings, Entry Ticket Fees, Schedule, Calendar, Discussion Topics, Venue, Speakers, A...
Description: Embodied AI, intelligent systems in physical forms such as humanoid and quadruped robots, is moving from spectacle to staffing plans.
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Embodied AI has arrived.. Humanoid and quadruped robots are moving off factory floors and into everyday operations, military deployments, and critical infrastructure. Technological advances in large language models LLMs and robotics are enabling robots to perform complex tasks autonomously. Security has not kept pace. Researchers have demonstrated that commercially available robots can be hijacked over Bluetooth, covertly exfiltrate audio, video, and spatial data to servers in China, and even infect neighboring robots wirelessly, forming physical botnets. If unaddressed, these security weaknesses are set to scale massively once humanoid robots are fully integrated into critical workflows. The risks need to be taken extremely seriously. A robot should be treated less like a machine on the balance sheet and more like a cyber-physical endpoint with cameras, microphones, radios, cloud dependencies, and motors. That means tougher procurement, tighter network controls, continuous vulnerability monitoring, and a credible plan for operational continuity if a fleet has to be pulled offline. Embodied AI, intelligent systems in physical forms such as humanoid and quadruped robots, is moving from spectacle to staffing plans. The shift is being driven as much by demographics as by technological progress. There are growing reports that the working-age population worldwide has begun to decline. China, an economic success story, has seen its population also decline again in 2025 as births hit a record low. These trends do not make large-scale automation inevitable, but they seriously strengthen the economic case for it in both corporate and government decision-making. The International Federation of Robotics identifies labor shortages, real-world testing of humanoid robots, and increasing attention to safety and cybersecurity as defining trends for 2026. Some early deployments of embodied AI reinforce this trajectory. BMW reports that the Figure 02 humanoid robot has assisted in the production of more than 30,000 X3 vehicles, while GXO and Agility Robotics describe their partnership (established in 2024) as “the first formal commercial deployment of humanoid robots.” In high-risk environments, Sellafield is deploying quadruped robots to reduce human exposure in nuclear decommissioning. Capital markets are also responding. Unitree filed for a reported $610 million initial public offering (IPO) in Shanghai in March 2026. Taken together, these signals suggest that robots are leaving pilot programs and becoming operational. That transition makes the security question immediate rather than theoretical. Unlike traditional IT assets, embodied AI systems combine multiple high-risk components in a single platform: cameras, microphones, sensors, wireless radios, cloud connectivity, and physical actuation. This convergence creates a broad and under-secured attack surface. A compromised robot can exfiltrate sensitive environmental and operational data, provide persistent remote access to internal networks, and interact physically with its environment, potentially causing unintended physical effects. This elevates robots from conventional endpoints to cyber-physical systems with both digital and real-world consequences. The risk is compounded by architectural choices. Many platforms rely on cloud-dependent telemetry, wireless provisioning interfaces, and centralized control mechanisms. These design decisions create multiple entry points for attackers and increase the likelihood of compromise across entire fleets of embodied AI systems. The risks are no longer theoretical. Documented vulnerabilities show that commercially available robots can be compromised with relative ease. Unlike traditional cyber threats, which mostly affect the digital world, exploiting robots enables attackers to manipulate the physical world, maximizing the potential for harm. In 2025, researchers discovered an undocumented backdoor in Unitree’s Go1 quadruped robot that enabled remote access via the CloudSail service. Axios reported that an exposed web application programming interface (API) could allow attackers to locate devices globally and, if a robot was online, view live camera feeds without authentication. Where default credentials remained unchanged, full device control was possible. Whether described as a backdoor or a design failure, the implication is the same: robots may be reachable in ways operators do not anticipate, just like any other Internet of Things (IoT) device. Further research disclosed a critical vulnerability in the Bluetooth Low Energy and Wi-Fi provisioning interface used by multiple Unitree models, including the Go2, B2, G1, R1, and H1 robots. According to both the UniPwn research and IEEE Spectrum, the flaw combined hard-coded cryptographic keys, trivial authentication bypass, and command injection in the Wi-Fi setup process. An attacker within radio range could obtain root-level access without physical contact, giving them control over the robot. Because the exploit propagates wirelessly, a single compromised device can enable lateral movement across nearby robots. This creates a fleet-level compromise scenario in which multiple units can be controlled simultaneously. The result resembles a physical botnet capable of both digital and physical actions. Surveillance risks are equally significant. Researchers wrote that the Unitree G1 robot continuously exfiltrated multimodal sensor and service-state telemetry every 300 seconds without the operator’s knowledge. This included streaming data to external servers, potentially including audio, video, and spatial mapping. A robot operating inside a plant or laboratory may therefore be mapping the environment in real time. The attack surface extends beyond firmware and networking layers. Researchers showed they could take control of a Unitree humanoid in about a minute, bypass its normal controller, and trigger physical actions. Demonstrations at GEEKCon in Shanghai indicated that both voice commands and short-range wireless exploits could hijack robots and propagate attacks to nearby units, including those not actively in use. At the software layer, embodied AI systems introduce additional risks due to their reliance on large vision-language models. Researchers demonstrated that physical-world text can influence system behavior, as injected visual prompts were shown to steer autonomous driving, drone landing, and tracking tasks without compromising the underlying software. This would enable threat actors to take control of a self-driving car or turn a drone into their own surveillance feed by embedding a visual prompt in the environment, such as hiding a message on a stop sign. The implications extend beyond individual devices to organizational and systemic risk. Embodied AI systems are already being deployed in environments where compromise has consequences beyond data loss. Manipulation or malfunction of robots during critical operations would have outsized economic or public safety consequences. Militaries are also experimenting with robotic systems (see Figure 4). In 2024, the Golden Dragon exercise between Cambodia and China featured robot dogs among the systems on display. Meanwhile, in the US, politicians have begun pushing for Unitree to be designated as a federal supply-chain risk, reflecting national security concerns about commercial robotics platforms. This is a very similar move to Poland’s ban on sensor-rich vehicles accessing military sites to limit surveillance risk. Ukraine has successfully deployed ground-based robots and drones in combat operations, marking a significant shift in modern warfare. In a landmark operation in April 2026, Ukrainian forces captured a Russian position using only unmanned systems — the first recorded instance of a robot-only assault in the conflict. As adoption scales, these risks become interconnected. A vulnerability affecting one platform or vendor could propagate across fleets, sites, or sectors, creating systemic exposure. At the same time, the pace of commercial development is outstripping regulatory oversight. Bank of America estimates that as many as three billion humanoid robots could be in operation by 2060. This convergence of demographic pressure, advancing AI capabilities, and falling production costs suggests that large-scale human-machine coexistence is highly probable. Figure 7: Summary of the factors fueling growth in robotics production, illustrated by Bank of America data (Source: Recorded Future) Securing embodied AI systems is therefore not a peripheral technical issue. It is a strategic requirement that must be addressed before widespread deployment locks in insecure architectures at scale. Table 1: Business risks associated with the adoption of insecure embodied AI systems (Source: Recorded Future) The mass-production surge: Established car manufacturers and tech giants are poised to accelerate their robotics ambitions, not only deploying robots on factory floors but increasingly manufacturing them at scale. As traditional vehicle sales potentially peak or decline due to demographic shifts, the expertise in mass production and complex assembly will almost certainly be repurposed to build robots. We should expect the bill-of-materials costs to continue their downward trend, meaning security features are increasingly marginalized in favor of market penetration. The inevitable breach: It is almost certain that we will see a major cyber-physical incident involving embodied AI in the next decade. This could take the form of large-scale operational downtime in a roboticized factory, a legal crisis arising from a hijacked robot causing human injury, or a high-profile case of industrial espionage involving a robot used to map a secret facility. The incident involving Ecovacs vacuums relaying obscenities and racial slurs from a remote hacker is an early indicator of how these risks may evolve. A new security industry: The next decade will likely see the rise of a dedicated industry focused on securing humanoid robots. Just as the PC era gave birth to antivirus software and the cloud era to SASE, the robotic era will require specialized firms that can provide "physical firewalling," behavioral motor-control monitoring, and "robot-specific" threat intelligence. Companies such as Periphery are examples of where the industry could be rapidly headed. Monitor and maintain a vulnerability register: Track disclosed vulnerabilities in any robotic platform your organization deploys or is considering. Establish a playbook for quickly patching or taking robots offline, and understand the operational downtime cost before, not after, a vulnerability is discovered. Recorded Future Vulnerability Intelligence can provide continuous monitoring of emerging CVEs and disclosures specific to embodied AI platforms. Communicate procurement risks to decision-makers: If your company is purchasing robotics for its operations, the risks of surveillance, covert data exfiltration, and remote compromise must be clearly documented and escalated to the board. Purchasing decisions based solely on unit cost, without accounting for tail risk, are not cost-effective. Interrogate manufacturers on security by design: Work as closely as possible with manufacturers to understand what security measures are built into the platform, what telemetry is collected, where it is routed, and how firmware updates are managed. If responses are unsatisfactory or opaque, treat that as a material factor in procurement. If the decision-makers' risk appetite remains high regardless, document it formally. Monitor the macro landscape continuously: New manufacturers are entering the market at a rapid pace, some with security as an afterthought. Recorded Future Threat Intelligence and Geopolitical Intelligence can assist organizations in tracking which companies are emerging, which have ties to state interests, and how the regulatory environment is shifting in key jurisdictions. Table 2: Mitigation strategies for business risks, with recommended ownership (Source: Recorded Future) Scenario: ACME Ltd manufactures high-grade munitions for a Western military and allied customers. To reduce human exposure to dangerous materials, it buys 100 humanoid robots from an overseas vendor. The chosen model is already used in comparable factories abroad and costs roughly half as much as an alternative sourced from the United States.
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LG robots move boxes in 90 seconds with zero human …
Description: LG demostrates autonomous humanoid and quadruped robots coordinating warehouse logistics without human control.
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From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. The platform centrally manages humanoids, quadrupeds, AMRs, AGVs, and wheel-based robots through one interface. A subsidiary of LG has showcased a future logistics workflow in which multiple autonomous robots collaborate without human intervention. In a demonstration, a bipedal humanoid lifted a box from a conveyor belt and passed it to a wheeled quadruped transport robot, which autonomously delivered it across the site. A wheel-type humanoid then used its extended arms to place the box on a shelf over two meters high. The entire process was powered by LG CNS’s Physical Works platform, with robots independently recognizing objects, making decisions, and coordinating tasks using Robot Foundation Model technology. Recently, LG Display also unveiled a 7.2-inch curved P-OLED humanoid robot display powered by its third-generation Tandem OLED display technology. LG CNS showcased its PhysicalWorks robot transformation (RX) platform through a live logistics demonstration at its Magok campus in western Seoul. During the event, four robots from different manufacturers completed coordinated warehouse tasks without remote control. The platform combines a simulation and video-based robot training module with a real-time orchestration system capable of assigning and reassigning jobs across mixed-brand robot fleets, according to The Korea Herald (TKH). During the demonstration, a humanoid robot picked up packaged goods from a conveyor and loaded them into a box. A wheeled quadruped logistics robot then transported the box across the workspace to a wheeled humanoid robot, which stacked it on a designated shelf. After completing the delivery, the quadruped returned to continue the logistics cycle while the wheeled humanoid placed an empty box back onto the conveyor for refilling. LG CNS also demonstrated adaptive task management by simulating an emergency scenario. When the quadruped robot was reassigned to patrol operations, the platform automatically deployed another autonomous logistics robot to continue the transport work without interrupting the workflow, according to TKH. The company said the robots operated entirely autonomously, relying on the platform’s robot learning and coordination capabilities rather than manual intervention. In the live setup, the robots transferred a single box between stations positioned roughly two to three meters ((6.5 to 9.8 feet) apart in about 90 seconds, with performance expected to improve further through additional field training and operational experience. LG CNS said the robotics industry is shifting beyond hardware-focused development toward software systems that allow robots to understand, coordinate, and reliably execute real-world industrial tasks. The company believes successful robot transformation depends on integrated learning, verification, and operational frameworks tailored to manufacturing and logistics sites rather than on individual robot performance alone, reports Chosun Biz. To support this strategy, LG CNS has developed its Physical Works platform, which combines two systems: Physical Works Forge for robot data learning and simulation-based verification, and Physical Works Baton for centralized control of robots from multiple manufacturers. The platform supports different robot types, including bipedal humanoids, quadrupeds, wheel-based robots, AMRs, and AGVs, through a unified management interface. LG CNS said the system reduces robot deployment timelines from several months to around one or two months. In mixed-robot environments of about 100 units, the company projects productivity gains exceeding 15 percent and operating cost reductions of up to 18 percent by minimizing traffic overlap, congestion, and manual intervention, Chosun Biz reported. The company has also invested in robotics and embodied AI firms developing humanoid control and robot foundation models. Currently, LG CNS is conducting proof-of-concept projects with 20 customers across industrial sectors while deploying the platform in South Korea’s Busan Smart City pilot project to manage patrol, cleaning, delivery, and service robots through a single system. Jijo is an automotive and business journalist based in India. Armed with a BA in History (Honors) from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. Premium Follow
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La française Genesis AI automatise les gestes complexes des robots
Description: Genesis AI dévoile GENE-26.5 pour automatiser les manipulations robotiques complexes. La start-up française cible les usages industriels, scientifiques et domestiques.
Description: Genesis AI unveils GENE-26.5, a robotic brain designed to help general-purpose robots perform complex physical tasks with human-level dexterity and coordination.
Description: Meta neemt de medewerkers en technologie over van een start-up die software bouwt voor intelligentie in mensachtige robots.
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Meta neemt de medewerkers en technologie over van een start-up die software bouwt voor intelligentie in mensachtige robots. Dat melden Amerikaanse media, waaronder Techcrunch en Quartz. Assured Robot Intelligence (ARI) bouwt sinds vorig jaar aan systemen die robots in staat stellen menselijk gedrag in complexe en dynamische omgevingen te begrijpen, te voorspellen en zich daaraan aan te passen. Het overgenomen team wordt onderdeel van Meta’s AI-lab, Superintelligence Labs. De start-up bouwt zogeheten foundation models voor mensachtige robots. Waar ChatGPT in de basis een taalmodel is, richt het werk van ARI zich op de bouw van een wereldmodel. Machines die de modellen en software gebruiken, moeten als het ware al kijkend de wereld om zich heen begrijpen. Met de overname slaat Meta hetzelfde pad is als Tesla, dat in samenwerking met xAI aan vergelijkbare robotische systemen werkt met wereldmodellen. Foto: Possessed Photography / Unsplash Uw e-mailadres wordt niet op de site getoond Een robot als maatje op de werkvloer wordt steeds minder een vergezicht en steeds vaker praktijk van de dag. In China zijn ze al redelijk betaalbaar en lopen de... PostNL houdt experimenten met bezorgrobots ter ondersteuning van de menselijke pakketbezorger. Na deze initiële testfase gaat de pakketbezorger de kaders stellen waar binnen het nieuwe technologie kan opschalen. Volgens een nieuw rapport zullen er over tien jaar naar schatting twee miljoen mensachtige robots aan het werk zijn, en driehonderd miljoen in 2050.
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Meta Acquires Robotics Startup To Boost Humanoid AI
Description: Georgia Tech's new AI system lets robots perform delicate tasks like folding towels and packing food with speed and accuracy exceeding human capabilities.
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Thanks to researchers at Georgia Tech, robots have taken several new steps towards replacing human labor – and not simply for dangerous tasks such as mining the depths of the Earth and exploring the Moon, or difficult tasks such as high-speed mass-assembly of thousands of cars. Instead, picture fine-motor, subtly complex tasks that have generally been beyond robotic dexterity and coordination: stacking cups, folding towels, packing food, and placing fruit onto plates – that is, the tasks of workers at hospitals, senior care facilities, child care centers, and restaurants. Now, if you’re a business owner who wants to pay nobody to do that work and pocket all the profit, you’ll be thrilled. If you’re the person who does such work, or your family members do, or you own a business serving people who do, or you live in a city whose tax-base depends on tax-payers who do such labor, you may see the replacement of humans differently. But first, let’s examine the genuinely remarkable technical breakthrough. In a recently-presented paper, Georgia Tech researchers Nadun Ranawaka Arachchige, Zhenyang Chen and colleagues explain how they have improved robots to perform domestic and retail work as accurately as, but more quickly than, people can. According to Shreyas Kousik, co-lead author on the study, he and his colleagues want to create a “general-purpose robot that can do any task that human hands can do." To make that work outside the lab, speed really matters – hence their innovation: the AI-based Speed Adaptation of Imitation Learning (SAIL) system. Drawing upon robotics, mechanical engineering, and machine learning, SAIL combines an algorithm to preserve consistent, smooth motion at high speed, high-fidelity motion tracking, self-adjusting speed based on motion complexity, and “action-scheduling” for latency in the real world. Compared to demonstration speeds in experiments of 12 simulated and two actual tasks, two different types of SAIL-enabled robotic arms worked up to four times faster in simulation and up to 3.2 times faster in reality. While designers have previously imbued camera- and sensor-using robots with offline Imitation Learning (IL) and Behavior Cloning to perform human-scale tasks, those systems had a limit: the speed of the human demonstration of the task for imitation. In turn, the demonstration speed limits bandwidth or throughput (the ratio of data output to data input) that industrial automation demands. SAIL smashes that barrier. Previously, working human-scale tasks more quickly that humans did was difficult for robots, because small environmental changes and robotic physical performance can change at high speed, resulting in errors and damage. As Kousik explains, “The challenge is that a robot is limited to the data it was trained on, and any changes in the environment can cause it to fail.” For instance, one of the experimental SAIL tasks was erasing a whiteboard. A stand-mounted whiteboard wobbles when wiped too quickly, but a human would automatically adjust for that change. Until now, robots didn’t adjust (which this barely related and hilarious video sort of demonstrates). “Understanding where speed helps and where it hurts is critical. Sometimes slowing down is the right decision,” explains Kousik, to which co-author Joffe adds, “The goal is not just to make robots faster, but to make them smart enough to know when speed helps and when it could cause mistakes.” To fulfill that goal, SAIL’s modules coordinate acceleration beyond training data, thereby maintaining smooth, fast, accurate motion and tracking, while adjusting speed as-needed and scheduling tasks according to hardware lag. So far, SAIL isn’t a panacea for robotic assimilation and acceleration of human activity, but it’s a significant step toward that goal. Which brings us back to the beginning, and the robotic job-pocalypse. According to the McKinsey Global Institute, by 2030, robots, AI, and other automation will terminate between 400 and 800 million jobs worldwide, which Robozaps says means “forcing up to 375 million workers (roughly 14% of the global workforce) to switch occupations entirely.” In the US alone, notes McKinsey, “30 percent of hours worked today could by automated by 2030” – that is, almost a third of the country. While some people claim that robots are no threat to employment, and if operating for public benefit could be a route towards universal basic income, other analysts highlight the complexity of trying to make such a technotopia possible. And that assumes the powers that be want such a world. If they don’t, who’s going to create 375 million jobs to prevent a global depression? As the Economic Policy Institute notes, when companies delete 100 retail jobs, an additional 122 people lose their jobs because those 100 retail workers can no longer buy as many goods and services. It’s even worse in manufacturing, because when corporations blow up 100 jobs, they indirectly double-tap another 744. Ultimately, robots won’t need to look or act like The Terminator to destroy civilization. They might just need to fold your towels. Source: Georgia Tech Sign up for our FREE daily New Atlas newsletter!
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At the 2026 AGIBOT Conference: Embodied AI Is Moving Into …
Description: The conversation around embodied AI (Robot as a physical interface for Artificial Intelligence) is evolving. While previous years focused on whether
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Geeky Gadgets The Latest Technology News 9:25 am April 25, 2026 By Roland Hutchinson The conversation around embodied AI (Robot as a physical interface for Artificial Intelligence) is evolving. While previous years focused on whether robots could move, perceive, and interact, the current question is whether these systems can operate reliably enough to integrate into real-world production. At its 2026 Partner Conference, the robotics company AGIBOT announced a shift toward an embodied AI “deployment phase,” moving the company toward developing systems built for reliable, real-world performance. The focus was on a cohesive, layered strategy that integrated products, models, deployment techniques, and ecosystem infrastructure. At the technical level, AGIBOT’s architecture is built around locomotion, interaction, and manipulation. The premise is that these capabilities cannot be treated in isolation if robots are to operate in real-world workflows. Movement enables access, interaction enables coordination, and task execution generates value. The company’s approach ties these together into a unified stack spanning hardware, perception, control systems, operating systems, and embodied AI models, with the aim of reducing fragmentation and speeding up iteration across the system. That integration is reflected in its current updated third-gen product lineup. AGIBOT has built a range of robot products covering humanoid, wheeled, and quadruped forms, each aligned with different operational environments. The positioning matches the form factor to the task. Alongside this, the company introduced six AI models aligned with the three intelligence layers, including motion-control models, multimodal interaction systems, and task-oriented models designed to handle longer, more complex operations. AGIBOT presented seven production solutions spanning manufacturing, logistics, commercial services, inspection, and cleaning, all framed as already operating in real environments. The distinction is important. These are not custom integrations, but standardized, repeatable solutions designed to scale. To support that shift, AGIBOT is building out infrastructure layers that extend beyond the robot itself. Its AIMA (AI Machine Architecture) ecosystem is intended to function as a full-stack development environment, lowering the barrier for deploying and customizing embodied AI systems. At the same time, the company introduced a large-scale data initiative and a global robot rental network, Sharebot, which allows partners to access robots as a service rather than through ownership. It reduces upfront costs, accelerates adoption, and creates a continuous loop in which deployment generates data, data improves models, and improved models feed back into deployment. Underlying all of this is a clear attempt to define the industry’s direction. AGIBOT outlined an “XYZ curve” as a framework for embodied AI development, with the past few years representing a phase where robots learned to move, and the coming years focused on whether they can consistently perform useful work. The company positions 2026 as the beginning of that transition. What APC 2026 ultimately presented was a system-level view of embodied AI. Robots do not operate in isolation, and neither can the systems that support them. The result is a reframing of what progress looks like, a system that can be deployed, iterated, and scaled. In that sense, the industry may be entering a phase where we finally get to see Artificial Intelligence becoming readily available in the physical world. Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
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Reconova targets embodied intelligence with robots deployed in airports
Description: The company builds on its background in computer vision technology.
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Written by Cheng Zi Published on 30 Apr 2026 5 mins read On April 29, at the third China Embodied Intelligent Robot Industry Conference and Exhibition, Reconova delivered a keynote on breaking through bottlenecks in the scenario-based commercialization of embodied intelligence. For the company, which has spent 14 years in artificial intelligence, the message was clear: machines are improving in their ability to understand the world, and may soon be ready to perform physical work. At a time when much of the industry emphasizes general-purpose capability and scale, it is positioning itself around real-world deployment and practical execution. Reconova was founded in 2012. Since then, it has operated through two distinct eras of AI. In the early phase, the central technical challenge was perception: enabling machines to interpret images, recognize objects, and understand scenes. This period coincided with the rapid expansion of deep learning, and intense competition among computer vision companies. Over time, that segment underwent a sharp consolidation. At its peak, thousands of companies claimed to compete in the space, capital flowed aggressively, and valuations rose quickly. A prolonged correction followed, marked by tighter financing, limited commercialization at scale, and increasingly homogeneous competition that compressed margins. Around 2019, many companies began to falter. Former unicorns were sold at discounts or shut down, outcomes that became increasingly common. At the time, security and finance were among the most targeted sectors. Reconova instead focused on less prominent use cases: passenger processing in civil aviation airports, commercial real estate applications in shopping malls, and driver assistance safety systems for commercial freight vehicles. From the outside, that appeared to be a conservative choice. That restraint, however, helped Reconova survive a period of high attrition while maintaining a leading market position, the company said. That focus appears to have translated into a defensible position. According to Frost & Sullivan, by 2024 revenue, Reconova ranked first in China’s visual intelligence products market for civil aviation enterprises, with an 8.9% share. Its products are deployed in roughly one-third of China’s civil airports. Among large hub airports handling more than ten million passengers annually, coverage rises to two-thirds. In the current AI cycle, the technical focus has shifted. Large models have expanded capabilities beyond perception to include action. For Reconova, this marks an inflection point. Jhan Dennis, founder and chairman of Reconova, described the transition: “Over the past 12 years, we have been building eyes, using vision to perceive and understand the physical world,” he said. “But now we are starting to move forward, toward the brain and the hands. On the basis of understanding the world, we are starting to make decisions, carry out execution, and help people get things done.” The company is expanding its focus from perception and cognition to decision-making and execution, with the aim of building a closed-loop system. It is positioning itself as a provider of embodied intelligence products for commercial scenarios and complex operations. Much of the current narrative around embodied intelligence emphasizes general-purpose capability. Systems that can adapt across multiple scenarios tend to attract stronger investor interest. That framing can disadvantage companies focused on vertical applications. Jhan takes a different view. General-purpose capability, he said, defines competition among platform companies and depends on scale, ecosystems, and early data network effects. In contrast, barriers in vertical scenarios are built through detailed understanding of workflows and repeated problem-solving with customers, not through model size alone. On the technical front, Reconova outlines a three-layer framework: Reconova’s commercialization strategy reflects these constraints. Jhan said complex, unstructured, and specialized scenarios are likely to reach commercial viability before general-purpose applications. General-purpose robots face dual constraints of technical capability and cost. They must generalize across tasks while meeting procurement thresholds for enterprise customers. Achieving both simultaneously remains difficult. In contrast, specialized systems can be optimized within known constraints, making them more viable commercially. Civil aviation is Reconova’s initial entry point for embodied intelligence. Its first deployment scenario is baggage handling. Baggage handling is among the most labor-intensive processes in aviation. Recruitment is difficult, turnover is high, and efficiency varies with weather and shift schedules. Airports have struggled with these issues for years. In practice, the environment presents multiple challenges. Baggage varies widely in shape and material, from rigid suitcases to soft bags and irregular items. Each requires different handling approaches. The physical environment is also inconsistent, with narrow pathways and tight equipment spacing, requiring real-time navigation. In addition, operations require close coordination between humans and machines, where delays in perception or decision-making could introduce safety risks. These constraints limit the effectiveness of general-purpose robots in such settings. While they may function across multiple scenarios, consistent performance in demanding environments remains difficult. Cost structures also limit their return on investment in labor-replacement use cases. Reconova has responded by developing a robot designed specifically for airport baggage handling. At the 2025 International Airport Expo, its AntOne robot demonstrated the ability to move and stack baggage of varying shapes in a simulated transfer zone. The company said the system incorporates a human-machine collaborative operating model. The robot performs repetitive transport and stacking tasks, while human workers intervene in edge cases. According to Reconova, this division of labor improves overall efficiency compared with fully manual operations. Jhan said pilot deployments at airports indicate that AntOne reduces labor dependence and physical strain on workers. He added that system throughput has increased by 30%, while baggage damage rates have declined to 0.12%. Reconova is conducting trials at multiple airports and plans to begin commercial deployment in the second half of the year. It is also exploring international markets, including Southeast Asia and the Middle East, where similar operational challenges exist. In a field often defined by broad ambitions, Reconova has taken a narrower approach, focusing on a specific problem and measurable outcomes in real environments. Within the embodied intelligence landscape, it does not fit neatly into either general-purpose robotics or traditional computer vision. It positions itself as a provider of systems designed for complex scenarios and precise physical operations. As with previous technology cycles, interest in robotics may fluctuate. Systems that demonstrate reliability in demanding conditions are more likely to persist. Reconova’s strategy, centered on depth over breadth, reflects that view and defines its position in the current market. KrASIA features translated and adapted content that was originally published by 36Kr. This article was written by Xiao Xi for 36Kr. Loading... Subscribe to our newsletters KrASIA A digital media company reporting on China's tech and business pulse.
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New social pet robot uses local AI to learn complex …
Description: The Familiar companion robot uses expressive movement and multimodal AI to interact with people beyond traditional task-based machines.
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From daily news and career tips to monthly insights on AI, sustainability, software, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation all with fewer ads or a completely ad-free experience. All Rights Reserved, IE Media, Inc. Colin Angle’s robot features a touch-sensitive coat, vision system, and microphones to interpret and respond to people. A new kind of robot is taking shape, and it is not built for factories. It is designed for people. At the Future of Everything conference, robotics pioneer Colin Angle unveiled a quadruped machine that focuses on interaction and companionship. The system, called a “Familiar,” represents a shift in how engineers approach physical AI. Angle, best known for cofounding iRobot and launching the Roomba, now leads Familiar Machines & Magic. The company has operated in stealth until now. The first Familiar is a four-legged robot designed to engage with people. It does not rely on a screen. Instead, it uses motion, sound, and touch. The machine features 23 degrees of freedom for expressive movement. Engineers added a touch-sensitive outer layer, along with cameras and microphones. These systems help the robot interpret its surroundings and respond naturally. Its onboard AI runs locally, using a compact multimodal model. This setup combines vision, audio, language, and memory in real time. The goal is to create behavior that evolves through repeated interaction. Angle said, “The next era of robotics is not just about dexterity or humanoid form – it’s about machines that can build and sustain human connection.” He added, “Today, we’re emerging from stealth to share our vision for systems that move beyond task execution and become a natural part of daily life.” Most investment in physical AI targets industrial use. Companies focus on robots that lift, sort, or transport goods. That market continues to grow rapidly. Angle’s team sees a different opportunity. They aim to build machines that people interact with daily. That requires a different design philosophy. Consumer-facing robots must understand context and emotion. They must respond in ways that feel intuitive. According to the company, physical presence plays a key role in this. FM&M argues that embodied systems can outperform screen-based AI in emotional tasks. People respond more strongly to physical agents than to chatbots. The team behind the project brings experience from major tech and robotics groups. Their background includes work at Disney Research, MIT, Amazon, and Boston Dynamics. Angle positioned the new robot as a step beyond earlier consumer machines. “iRobot proved that robots could deliver value at scale,” he said. “But they were still task machines.” “My goal has always been to create systems that understand context, remember interactions, and behave with consistency over time. That’s what we’re doing at Familiar Machines & Magic.” Unlike humanoid robots, the Familiar avoids human-like form. Engineers chose a quadruped design to improve approachability and movement. The focus remains on presence and interaction. The company has not announced a release timeline. It also has not detailed specific use cases. Today’s reveal marks a technology preview, not a product launch. Still, the direction is clear. FM&M wants to scale robots that people choose to live with. The company emphasizes on-device AI to reduce latency and protect privacy. The Familiar suggests a shift in robotics. Instead of machines that complete tasks, engineers are building systems that build relationships. Aamir is a seasoned tech journalist with experience at Exhibit Magazine, Republic World, and PR Newswire. With a deep love for all things tech and science, he has spent years decoding the latest innovations and exploring how they shape industries, lifestyles, and the future of humanity. Premium Follow
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POCO soft robot companion rethinks human-AI connection
Description: poco is a soft robotic companion that examines alternative relationships between humans and artificial intelligence.
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POCO is a soft robotic companion developed by designer Mehrnaz Amouei that examines alternative relationships between humans and artificial intelligence. The project focuses on interaction models based on presence, responsiveness, and clearly defined limitations, rather than systems that interpret or direct emotional states. As AI increasingly operates within personal and emotional contexts, questions emerge around dependency, interpretation, and control. POCO addresses these concerns by proposing a relational framework in which the system functions alongside the user without assuming authority. Instead of diagnosing or guiding behavior, the device is designed to respond, reflect, and maintain boundaries that remain visible to the user. The project is informed by a year-long research process involving qualitative interviews, interdisciplinary input, and iterative prototyping. Findings indicated a preference for systems that offer availability and responsiveness without projecting certainty or control. In response, the design introduces the concept of ‘constructive interdependence,’ where limitations are embedded as part of the interaction model. The system communicates what it can and cannot do through its behavior and states. POCO’s form remains ambiguous, between object, creature, and companion | all images courtesy of Mehrnaz Amouei Physically, POCO is developed as a soft, tactile object that connects to a smartphone, which functions as its computational component. Interaction is based on touch, using capacitive sensors that respond to gestures such as holding or stroking. Movement is expressed through slow, rhythmic actions that resemble breathing, reinforcing a sense of presence without relying on mechanical articulation. Engagement with the device is structured as a reciprocal process. The system does not initiate interaction independently but responds to user input, establishing a shared rhythm. States of activity and rest are visibly communicated, reinforcing awareness of the system’s operational boundaries. Through its integration of material, behavior, and interaction logic, POCO robot companion positions AI as a participant within a relational system rather than a directive tool. The project frames trust not as a function of expanded capability, but as a result of transparency, limitation, and balanced interaction between user and device. robot’s variations adapt to users, environments, and emotional preferences while maintaining a consistent identity a soft textile body wraps around the device, transforming rigid technology into a huggable presence at human scale, POCO moves through spaces as a quiet presence, less a device and more a companion that belongs a soft robotic companion designed as a quiet emotional presence that integrates into everyday life a prototype setup showing the robotic device’s tactile interface a working prototype explores tactile interaction, and how AI can exist in a physical, touchable form project info: name: POCO | Soft Robotic Companion for Everyday Life designer: Mehrnaz Amouei | @minazez designboom has received this project from our DIY submissions feature, where we welcome our readers to submit their own work for publication. see more project submissions from our readers here. edited by: christina vergopoulou | designboom happening now! florim brings a sense of handcrafted authenticity to contemporary architectural surfaces, presenting sensicolore.
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Whale Cloud and AGIBOT Announce Strategic Partnership to Accelerate Global …
Description: Whale Cloud, a global leader in providing full-stack digital and intelligent capabilities for telecommunications and enterprise customers, and AGIBOT, a ...
Description: Meta suma talento en robótica para desarrollar sistemas que permitan a futuros humanoides moverse y adaptarse a entornos reales.
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Dispositivos que han pasado por nuestras manos. Aquí podrás encontrar todo lo nuevo en tecnología móvil. ¿Tienes dudas? Estás en el lugar indicado. Rumores del mundo móvil. Nada está confirmado. Entrevistas a personas notables dentro del mundo de la tecnología ¿Te gusta jugar? Aquí podrás revisar reviews, noticias y más. Meta compró Assured Robot Intelligence, una startup que desarrolla IA para robots, como parte de un plan más amplio para entrar en el mercado de robots humanoides, según detalló Bloomberg. La operación se cerró el viernes y no se revelaron los términos financieros. La adquisición suma a Meta un equipo especializado en crear sistemas capaces de ayudar a los robots a entender lo que ocurre a su alrededor, anticipar conductas humanas y adaptarse a espacios cambiantes. La compañía busca avanzar en máquinas con forma humana que puedan moverse como personas y apoyar tareas físicas. Assured Robot Intelligence le permitirá a Meta sumar experiencia en IA aplicada al control de robots humanoides. La compañía pretende desarrollar máquinas capaces de coordinar movimiento, percepción y aprendizaje, en un sector donde también están trabajando empresas como Tesla, Google y Amazon. El equipo de la firma adquirida, incluidos sus cofundadores Lerrel Pinto y Xiaolong Wang, se integrará a Meta Superintelligence Labs. Del mismo modo, trabajarán junto a Meta Robotics Studio, grupo creado el año pasado para desarrollar la tecnología base de futuros robots. La compra no apunta solo a investigar robots dentro de Meta, sino que, además, la firma también trabajaría en hardware humanoide propio, sensores, software y sistemas de IA que podrían quedar disponibles para otras compañías del sector. La trayectoria de los fundadores refuerza el interés de Meta por sumar experiencia directa en robótica avanzada: Meta va más allá de fabricar sus propios robots; además, buscará crear una base tecnológica que otros fabricantes puedan adoptar, como ocurrió en los teléfonos móviles con Android y los chips de Qualcomm.
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Meta acquires Assured Robot Intelligence to build the Android of …
Description: Meta bought ARI, a robotics AI startup, and folded it into Superintelligence Labs. The goal: be the platform every humanoid manufacturer needs.
Description: Meta acaba de adquirir Assured Robot Intelligence (ARI), una startup especializada en IA para robots, con el objetivo declarado de resolver "los desafíos críticos de los mercados laborales de alto valor". Lo publica Mariella Moon en Engadget este 2 de mayo. El precio de la operación no se ha revelado, pero el movimiento es estratégico: Meta compra Assured Robot Intelligence para construir el software que gobierne humanoides. El equipo fundador se une a Superintelligence Labs. Sin precio revelado.
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Publicado el 4 mayo, 2026 Meta acaba de adquirir Assured Robot Intelligence (ARI), una startup especializada en IA para robots, con el objetivo declarado de resolver «los desafíos críticos de los mercados laborales de alto valor». Lo publica Mariella Moon en Engadget este 2 de mayo. El precio de la operación no se ha revelado, pero el movimiento es estratégico: el equipo al completo de ARI, incluidos sus tres cofundadores, se incorpora a los Superintelligence Labs de Meta, el nuevo laboratorio de IA que dirige Alexandr Wang. Mark Zuckerberg quiere construir el sistema operativo de los robots humanoides. Y va en serio. ARI es una startup fundada por Xiaolong Wang, Xuxin Cheng y Lerrel Pinto, tres investigadores de robótica con perfiles académicos notables. Pinto también cofundó Fauna Robotics, que fue adquirida por Amazon para su propio proyecto de robots humanoides, lo que da idea del nivel del equipo que Meta se acaba de llevar. El objetivo de la empresa era construir lo que Wang llama un «agente físico de propósito general», es decir, un sistema capaz de aprender directamente de la experiencia humana y ejecutar tareas en el mundo real con un cuerpo humanoide. En un post en X, Wang explicó que desde el principio sabían que ese agente tendría que ser humanoide y que el escalado llegaría a través del aprendizaje directo de lo que hacen las personas. La arquitectura técnica de ARI se centra en el control de todo el cuerpo (whole-body humanoid control) y en el aprendizaje autónomo, los dos problemas más difíciles de resolver en robótica moderna. No es visión por ordenador clásica: es enseñar al robot a moverse como un humano, con toda la complejidad que eso conlleva. El CTO de Meta, Andrew Bosworth, lleva al menos desde 2025 articulando una visión muy clara: Meta quiere ser el Android de la robótica. No fabricar robots, sino crear el software que otros fabricantes puedan licenciar. La misma apuesta que hizo Google con los móviles, pero para humanoides. «El software es el cuello de botella», dijo Bosworth, que proyectaba empezar con una mano robótica con destreza y escalar desde ahí. La adquisición de ARI encaja perfectamente en esa hoja de ruta: el equipo trae experiencia profunda en cómo diseñar modelos y capacidades de frontera para el control robótico. Meta no parte de cero. Cuenta con Superintelligence Labs, liderado por Alexandr Wang (el mismo que antes dirigía Scale AI), ya investido como Chief AI Officer. Tiene recursos de infraestructura masivos, acceso a datos a una escala que pocas empresas en el mundo pueden igualar y una estrategia de IA abierta (familia Llama) que le ha granjeado una comunidad de desarrolladores. Para un problema que requiere aprender de la experiencia humana, eso es un activo enorme. El momento no es casual. Hay al menos tres competidores directos con movimientos relevantes en los últimos meses. Amazon acaba de quedarse con Fauna Robotics, cofundada por el mismo Lerrel Pinto que ahora se va a Meta, para construir su propia flota de robots en almacenes y centros de distribución. Tesla lleva años desarrollando el robot Optimus y ha tomado la decisión, anunciada este año, de dejar de producir los modelos S y X en su fábrica de Fremont, California, para reconvertir ese espacio en líneas de producción de robots humanoides. Figure AI, 1X y Apptronik también están en carrera, con rondas de inversión multimillonarias en los últimos dos años. El hardware de los robots mejora rápido. Según el razonamiento de Bosworth, eso hace que el software sea el diferenciador real. Quien resuelva primero el control general del cuerpo humanoide a escala tendrá una ventaja estructural difícil de replicar. Llevo cubriendo movimientos de Meta en IA desde que se llamaba Facebook AI Research en 2013, y pocas veces he visto a la empresa comprar algo tan claramente alineado con una estrategia a diez años. La adquisición de ARI no es una apuesta especulativa: es la pieza que faltaba para darle credibilidad técnica a la visión del Android robótico. Lo que más me convence es la coherencia del equipo. Pinto viene de fundar Fauna Robotics (Amazon lo quiso), Wang ha publicado trabajo puntuado en NeurIPS y Cheng ha trabajado en sistemas de locomoción de última generación. Cuando los tres se van juntos a la misma empresa, es porque creen en la visión. Lo que más me preocupa es el timing competitivo. Meta ya recortó 8.000 empleados en mayo de 2026 y reorientó toda su estructura hacia la IA, apostando a que los ahorros de plantilla financian la infraestructura futura. Si el negocio de robots tarda más de cinco años en generar ingresos reales, la narrativa interna se complica. Y Amazon, con la distribución de Fauna Robotics y una cadena de suministro que ya existe, tiene una ventaja operativa real sobre una empresa que arranca desde el software. La pregunta a doce meses no es si Meta puede construir buenos modelos para robots. Es si puede construirlos más rápido que Tesla, que tiene hardware propio, instalaciones de producción reconvertidas y un dataset de conducción autónoma que lleva años generando datos del mundo físico. La vigilancia de los empleados de Meta para entrenar agentes de IA y el plan de Superintelligence Labs son señales de que Zuckerberg entiende que el dato es el moat. Si consigue que los humanoides aprendan de las interacciones humanas a escala, la apuesta puede funcionar. Si no, habrá pagado muy caro por talento que tardará años en producir algo con nombre. ARI desarrolla IA para control de robots humanoides, con foco en el control de todo el cuerpo y en sistemas de aprendizaje que permiten al robot aprender directamente de la experiencia humana, sin depender de programación manual de cada movimiento. No se han revelado los términos económicos de la adquisición. Engadget confirmó el movimiento a través de un portavoz de Meta que citó Bloomberg como primera fuente. Tesla apuesta por el hardware propio con el robot Optimus y una planta de producción dedicada, mientras Meta quiere desarrollar el software que licenciar a fabricantes terceros, siguiendo el modelo Android. Son estrategias complementarias, aunque ambas compiten por el mismo talento y los mismos datos de entrenamiento. por Natalia Polo Análisis diario, herramientas y tutoriales sobre IA en wwwhatsnew.
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GitHub - amitb-quantum/roboapi: The unified API layer for robotics. Connect …
Description: The unified API layer for robotics. Connect any robot, any brand, with one SDK. Like Stripe, but for robots. - amitb-quantum/roboapi
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We read every piece of feedback, and take your input very seriously. To see all available qualifiers, see our documentation. The unified API layer for robotics. Connect any robot, any brand, with one SDK. Every robot manufacturer ships a different SDK, a different protocol, and a different data format. A Boston Dynamics Spot speaks nothing like a Universal Robots UR5. A Figure humanoid has nothing in common with an Agility Robotics Digit. Every team building on top of robots rewrites the same integration layer from scratch. This is a multi-billion dollar tax on the robotics industry. RoboAPI is the Stripe for Robotics — a single unified API that abstracts every robot into one clean developer experience. One SDK. One API key. Every robot. That's it. No ROS knowledge required. No brand-specific SDKs. No protocol translation. Browse the full interactive API docs at http://localhost:8000/docs RoboAPI connects to any ROS2 robot via rosbridge. The turtle draws a full circle — driven entirely through the unified RoboAPI layer. 🐢 RoboAPI uses a pluggable adapter pattern. To add any robot: Register it in adapters/__init__.py and it's immediately available through the full API. RoboAPI is in early development and we welcome contributions — especially: See CONTRIBUTING.md for guidelines. The robotics industry is at its Stripe moment. Before Stripe, every company built custom payment integrations. Before Twilio, everyone wrote their own SMS stack. The pattern is always the same: fragmented, complex, infrastructure problem → one abstraction layer wins → everything builds on top. Robotics is there right now. Dozens of manufacturers, dozens of protocols, thousands of teams rebuilding the same middleware. RoboAPI is the abstraction layer. MIT — see LICENSE 🚧 Early development — API may change. Not yet recommended for production. ⭐ Star this repo to follow progress. 💬 Open an issue to request a robot adapter or report a bug. Built with FastAPI · ROS2 · roslibpy The unified API layer for robotics. Connect any robot, any brand, with one SDK. Like Stripe, but for robots. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.
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Think Robots Are Impressive Now? Just Wait Until They Have …
Description: This next-generation network technology won't just make our phones faster; it'll unlock new capabilities in robots, turning them into all-sensing, always-learning fleets.
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This next-generation network technology won't just make our phones faster; it'll unlock new capabilities in robots, turning them into all-sensing, always-learning fleets. The confluence of two seemingly distinct technologies will result in new capabilities for robots. Why are there so many robots at a show focused on phones? This is the question I asked myself as I roamed the halls of Mobile World Congress, on the lookout for the most exciting technology that will define the next few years. The first and most obvious answer is that robots draw crowds. A dancing humanoid is an easy way to attract people to your booth. But to see the robots at this year's MWC purely as a publicity stunt would be to ignore the bigger conversation happening around robots and connectivity. Already in 2026, we've seen major leaps forward in robotics, with companies including Boston Dynamics and phone-maker Honor showing off humanoid robots designed for industry and home environments. But there is yet another level to unlock, and it relies on 6G -- the next-generation network technology set to succeed 5G in 2030 and beyond. On the surface, 6G and robotics might seem distinctly unrelated -- beyond being technologies of a future that we're not living in quite yet. But in this future, 6G will open new doors for humanoid robots that'll transform them from clunky, standalone mechanical figurines into efficient fleets, where individuals will form part of an all-sensing, always-learning ecosystem. This will happen first in industry, then in hospitality and care environments, before potentially landing in our homes. It's an exciting prospect, but as the experts I spoke to at MWC last month cautioned, there'll be some big leaps in technology required before they, and we, are ready for that. To understand how 6G will unlock new possibilities for robots, let's start with the special capabilities the network technology will have. The first is that 6G will act as a sensor network, with sensors embedded into both the robots and their environments, Qualcomm's executive vice president of Robotics Nakul Duggal told me. This allows the 6G radio to act like radar -- constantly scanning and mapping its surroundings in real time to detect obstacles. Imagine a robot attempting to navigate a crowded environment: The 6G network should quickly and cheaply help create a kind of virtual map for it to do so safely. Second, there's the pure speed at which 6G will communicate vast reams of data. The 5G networks we currently use aren't necessarily built to handle AI requests, but the 6G networks will be, providing a consistent, low-latency, relatively low-power way to process intelligence and deliver that intelligence to robots, according to Frank Long, associate director of intelligent services at deep tech research firm Cambridge Consultants. Private 5G networks combined with edge AI (relying on devices for computing, not just the cloud) can fill the gap for now, but public networks, not so much. By contrast, Long said, "with 6G you can pretty much have that quality of service guarantee." Cambridge Consultants brought a demo of an autonomous humanoid robot to MWC that can pick up and place down a box based on where it sees you pointing. The gesture recognition, plus the ability to react in real time, while varying its grip to pick up something that might be on an angle, requires an enormous amount of compute power. (The demo was powered by a private 5G network.) The robot was able to pick up this box and place it on a spot I pointed to. Whether robots are connected to the cloud, or to each other in a peer-to-peer fleet, the network will need to handle their intelligence demands at speed. For robots to be constantly talking to the infrastructure around them -- and to each other -- a strong, reliable uplink will be required, explained Anshuman Saxena, general manager of robotics at chipmaker Qualcomm. He gave the example of two robots working in a retail environment where one is unloading soda cans from a truck, and another is restocking shelves. They'll need to align on how to read the space around them to complete each task, including understanding how many cans will need placing, and when they'll be ready. "The only way is this robot, while shelving, goes to the back door entry of the truck that is getting unloaded and sees what is available," said Saxena. "Or the robot that's unloading is communicating the bigger picture to every other robot, so that we have a view of where the things are placed, so that they can plan." This is what's known as long-horizon planning, where a robot isn't just focusing on the immediate task but thinking about how that task fits into a broader context over a longer timeframe within a dynamic and unstructured environment. In other words, it's performing the kind of ongoing mental multitasking that humans do on a daily basis, reacting at speed to what's going on around us, while also considering what's next. In the Cambridge Consultant demo, the robot was capable of thinking 16 steps ahead. Meanwhile, lightning-fast 6G will help robots make split-second decisions, based on feedback not just from their own sensor-packed bodies, but from other robots and tech in the environment. "The retail stores have cameras," said Saxena. "It's not a robot, but it can be the eyes of the robot." In your own home, you might have only a single humanoid robot. But that won't be as different from the retail scenario as you may think. That's because many of the devices you own, including your phone and security cameras, can already communicate with each other, and the robot will be just another one in the mix. Or maybe you'll have one humanoid and a bunch of smaller robots designed for specific tasks. "There is a fleet aspect in the products that we use," Duggal said. "You don't feel that, but that is exactly how the product is working." Keep in mind that your phone is both a physical object itself and all the software and data that are managed elsewhere. The phone also provides feedback to refine that software, as will the 6G-equipped robots. "So a robot is going to be performing a certain physical task, and while it may perform it in your home, if it's also performing the same task in many other homes, there is this aspect of learning and deployment," Duggal said. This continuous learning is perhaps one of the biggest challenges that 6G is expected to help solve in robotics. Robots and AI will need massive amounts of real-world data that today's networks can't keep up with, even for mundane tasks. For example: picking up and serving you a cup of coffee, which involves dexterity and balance, with the added element of heat. A robotic arm might not care about the temperature. "But if it is hot, how would we react?" said Saxena. "We would just quickly leave it, which is a very fast reaction time." The speed of 6G networks will be essential. By the time a robot arrives in our homes, we will want to know that it shouldn't hand us a scalding-hot drink and how to protect itself from damage. Much of this learning might have taken place in hotels or restaurants, where overnight, robots load and unload dishwashers and reset the kitchen. The robot will bring that training into your home, where it'll still need to further learn about your unique layout and routine. This will likely be a time-consuming process. Qualcomm is working with several robotics companies, including Neura Robotics, which develops robots for both industrial and home use. "It's going to be incredibly challenging," said Long. "Put it this way, members of my immediate family still struggle with opening the baby gate in my stairs, even after extensive training. So a robot, I think, might be a few years away from opening that baby gate." But 6G is not expected to roll out widely until at least 2030. What are the robots that companies are already building and deploying to do until then? They're making the leaps and bounds they can with the networks of today. "So you're not waiting for 6G," Saxena said, "but when the connectivity comes along, you are talking about experiences which can be way beyond what robotics can do [today]." While the confluence of robotics and 6G will indeed unlock some hitherto unseen next-level robotics, there is plenty that robots can learn in the meantime -- particularly when it comes to improving dexterity -- to prime them to take advantage of better connectivity. That's especially true if we're ever to consider inviting humanoids into our homes, an idea that feels, at least for now, like something worth delaying until at least the 6G-enabled 2030s -- if not beyond.
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Robots in Chinese literature circa 1902 | The Tangled Woof
Description: The concept of the "robot," a mechanical replacement for a human worker, seems to have been one of those things that was just in the air at the turn of the twentieth century, across the world. As is now well known, the English word was coined by the Czech writer Karel Capek (who credited his…
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The concept of the “robot,” a mechanical replacement for a human worker, seems to have been one of those things that was just in the air at the turn of the twentieth century, across the world. As is now well known, the English word was coined by the Czech writer Karel Capek (who credited his brother Josef for the inspiration, from the Czech word robota, forced labor). In the interesting short article, “Techno-Utopias And Robots In China’s Past Futures” in the new, free anthology Proletarian China: A Century of Chinese Labour, Craig A. Smith details the early history of robots in Chinese literature, which is not completely unlike the Western science fiction of the day. Here are some excerpts: The idea of animated or mechanical humanoid servants and labourers appeared in classical Chinese texts. Mozi, a utilitarian philosopher active in the fifth century BCE, even created mechanical birds and beasts, and is now the namesake of a technology company. However, the concept of a ‘machine-man’ (机器人, the modern Chinese word for robot) only made its way from elite texts into the popular imagination towards the end of the Qing Dynasty. Around the turn of the century, the entire world became fascinated with the idea of humanoid automatons and their potential for labour. The most memorable example of this in the West is the Tin Woodman from The Wonderful Wizard of Oz (1900), a depressed cyborg lumberjack yearning for a heart. Chinese fiction was in step and introduced labour automatons but with decidedly Chinese characteristics. In 1905 and 1906, the newspaper Southern News serialised a lengthy novel by Wu Jianren entitled The New Story of the Stone (新石头记). Although other Chinese science fiction writers penned stories with automatons at the time, Wu’s novel was a wonderland, its plot following Jia Baoyu, the protagonist of the eighteenth-century Dream of the Red Chamber (红楼梦), China’s most famous novel, into a twentieth-century technological utopia. Passing through a technological device called a ‘civilisation mirror’ (文明镜), Jia enters this utopia and is immediately served tea by a talking automaton ‘boy’ servant. The journey then proceeds through a melange of advanced technologies, including flying machines and submarines. It might have been around this time that Kang Youwei wrote the Book of Great Unity (大同书). The complete volume did not appear in regular print until 1935, eight years after his death, leading to controversy and numerous studies on the dating of the text. Tang Zhijun’s extensive research has shown that Kang most likely finished his manuscript in 1902, a finding corroborated by Wang Hui. Building on a few short chapters from [the Confucian classic] The Book of Rites (礼记), and contextualising these ideas within the modern reality of nation-states and new political economies, Kang envisioned a future world with no suffering. He saw robots playing an important role in his Confucian utopia, yet his position as a member of the literati class shaped his understanding of how robots would bring an end to the traditional hierarchies: ‘There will be no slaves or servants, but their functions will be performed by machines, shaped like birds and beasts.’ Kang imagined that ‘in the time of the Great Peace, there will be no suffering. Labourers will only find enjoyment.’ This will be possible because they will only put their skills to use in creating works ofart, as the heavy lifting will all be done by robots. Like H.G. Wells, Kang saw technological advancements bringing an end to toil and opening the door to universal leisure: ‘One will order by telephone, and food will be conveyed by mechanical devices—possibly a table will rise up from the kitchen below, through a hole in the floor. On the four walls will be lifelike, “protruding paintings”.’ This great trust in the emancipatory potential of science continued throughout the twentieth century, and revolutionaries, including Mao Zedong in his youth, found Kang’s work inspirational. However, largelydue to his promotion of constitutional monarchy, Kang is now remembered as a conservative opponent of revolution. Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.
Description: Explore the convergence of AI and robotics! Discover how AI is transforming robots into intelligent systems, enabling autonomous action and real-world impact.
Description: Zalando is supercharging its logistics backbone with the roll-out of up to 50 AI-driven Nomagic robots across its European fulfilment network. This expansion al...
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Dogonić legendę. Chiński humanoid od Unitree biega niemal tak szybko …
Description: Jesteśmy coraz bliżej momentu, w którym roboty zaczną poruszać się z gracją i prędkością profesjonalnych lekkoatletów. Jedną z firm, która w niebezpiecznie
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Najnowsze nagranie jest tego dowodem. Widzimy na nim robota, który podczas testów na bieżni lekkoatletycznej osiągnął zawrotną prędkość sprintu wynoszącą 10 metrów na sekundę. Na tym oczywiście nie koniec, bo cel firma ma ambitny – pobić rekord ustanowiony przez Usaina Bolta. Osiągnięcie prędkości 10 m/s (co przekłada się na około 36 km/h) to wynik, który oszałamia i niebezpiecznie zbliża maszynę do rekordu świata, który Usain Bolt ustanowił w 2009 roku – 100 metrów w 9,58 s ze średnią pęskością 10,44 metrów na sekundę. Robot Unitree jest więc o krok od dorównania legendzie. Co ciekawe, urządzenie pomiarowe na bieżni wskazało w pewnym momencie nawet 10,1 m/s, choć firma zachowuje chłodną głowę i zaznacza, że mogło dojść do drobnego błędu pomiarowego. Żeby jeszcze lepiej uzmysłowić Wam skalę postępu w tej dziedzinie – zaledwie rok temu rekord świata dla pełnowymiarowych humanoidów wynosił skromne 3,3 m/s. Skok wydajności, jakiego dokonało Unitree w ciągu kilkunastu miesięcy, jest po prostu bezprecedensowy. Firma zdetronizowała słynnego Atlasa od Boston Dynamics, który poruszał się z prędkością około 2,5 m/s. Ale plany firmy są jeszcze większe, bo zgodnie z zapowiedziami, jeszcze w tym roku zobaczymy, jak ich maszyny złamią barierę 10 sekund w biegu na 100 metrów. Czytaj też: Robot z AliExpress? Unitree wprowadza model R1 na globalny rynek Unitree nie jest jednak jedyną firmą, która chce uczynić ze swoich robotów prawdziwych sprinterów. Rywalizacja w Chinach przypomina prawdziwe igrzyska olimpijskie dla maszyn. Podczas World Humanoid Robot Games 2025, model Tien Kung Ultra wygrał bieg na 100 metrów z czasem 21,50 sekundy, a w kwietniu zeszłego roku ten sam robot ukończył pierwszy na świecie półmaraton dla humanoidów w czasie 2 godzin i 40 minut. Z kolei w lutym tego roku firma MirrorMe zaprezentowała model Bolt, który przy wzroście 175 cm również potrafi rozpędzić się do 10 m/s. Czytaj też: Panther to pierwszy robot humanoidalny, który naprawdę posprząta Twój dom Na tym nie koniec, bo już 19 kwietnia odbędzie się druga edycja Humanoid Robot Half Marathon w Pekinie, gdzie ponad 70 zespołów przeprowadzało nocne testy na torach w strefie technologicznej. Eksperci przewidują, że masowy start kilkudziesięciu robotów biegających ramię w ramię będzie widokiem, który na stałe zmieni nasze postrzeganie robotyki. Źródło: Unitree Portal technologiczny z ponad 29-letnią historią, piszący o nauce i technice, smartfonach, motoryzacji, fotografii. Technologie mamy we krwi!
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Walmart-backed robotics group Symbotic in $4.5bn talks to merge with …
Description: A supplier of robots to Walmart distribution centres is in talks to merge with a SoftBank-sponsored special purpose acquisition company that would value it at a...
Description: Japanese investor has raised multiple Spacs and has been looking for a deal as market cools
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SoftBank to acquire ABB’s robotics arm in $5.4 billion deal: …
Description: SoftBank Group will buy ABB's robotics business for $5.4 billion, aiming to integrate robotics with artificial intelligence. The move follows ABB's struggles in sales and profitability within the robots unit.
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SoftBank Group has agreed to acquire the robotics business of Swiss engineering group ABB for $5.4 billion, marking a major step in the Japanese investor's strategy to merge robotics and artificial intelligence (AI), which Founder and CEO Masayoshi Son calls “Physical AI.” This acquisition, announced on Wednesday, is the latest effort by CEO Son to establish Softbank as a core player in the development of artificial intelligence. Although SoftBank pushed into humanoid robotics a decade ago with its Pepper robot before scaling back, its recent investments in the sector include Berkshire Grey and AutoStore, alongside leading a $40 billion funding round in ChatGPT-maker OpenAI and $6.5 billion purchase of chip design company Ampere in March, news agency Reuters reported. The deal means ABB has abandoned its original decision to spin off and separately list its industrial automation business, which competes with Japan's Fanuc and Yaskawa, as well as Germany's Kuka, in the manufacturing of factory robots. The decision is the first major move under ABB CEO Morten Wierod, who took charge last year, following years of struggling sales and falling profitability within the robots unit. The robotics division, which employs 7,000 people and generated sales of $2.3 billion last year or 7% of ABB's total revenues — saw limited operational crossover with the rest of the company, which primarily focused on electrification and automation. ABB announced to shareholders in April its decision to spin off robotics but decided to sell instead because the SoftBank deal provided money immediately, Wierod told Reuters. The acquisition is expected to close in mid- to late-2026 and will generate cash proceeds of roughly $5.3 billion, ABB told Reuters. "We always said that robotics is a market with much higher volatility. And that's what we've seen over the years, both when it comes to growth, but also margins," Wierod said. “So it is a bit of a different market than, say, the rest of ABB today, which is focusing on electrification and automation.” ABB will spend the proceeds from the sale on developing new technology and production capacity in electrification and automation, and may also consider funding new acquisitions, Wierod said. "We do have firepower to also do bigger acquisitions, so we're not excluding bigger deals," he added. Catch all the Business News , Corporate news , Breaking News Events and Latest News Updates on Live Mint. Download The Mint News App to get Daily Market Updates. Download the Mint app and read premium stories Log in to our website to save your bookmarks. It'll just take a moment. Oops! Looks like you have exceeded the limit to bookmark the image. Remove some to bookmark this image.
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Softbank Takes Over ABB Robotics In A $5.4 Billion Deal …
Description: SoftBank Group (OTC: SFTBY) (OTC: SFTBF) said on Wednesday it will buy the robotics division of Swiss engineering firm ABB (OTC: ABBNY) in a $5.4 billion deal, as the Japanese firm looks to boost its artificial intelligence portfolio.
Description: SoftBank Robotics said it will start sales of its commercial flame-cooking robot, FLAMA, in Japan, with exclusive domestic sales handled by subsidiary Gourmet X.
Description: Food service robot startup Bear Robotics has raised $81 million (roughly Rs. 616 crore) in a Series B funding round with investors that include Cleveland Avenue...
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Softbank to merge with AI robotics company in which it …
Description: SoftBank Group Corp. plans to buy the remaining portion of AI and robotics developer Berkshire Grey Inc. that it doesn’t already own in a roughly $375...
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SoftBank-backed Bear Robotics raises $81 mln for waitering robot rollout
Description: SoftBank Group Corp-backed food service robot startup Bear Robotics has raised $81 million in a Series B funding round with investors that include Cleveland Ave...
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Canon Solutions America and SoftBank Robotics America Partner to Expand …
Description: MELVILLE, N.Y., June 9, 2021 /PRNewswire/ -- As part of its commitment to expanding its digital automation solutions and services, Canon Solutions...
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SoftBank Robotics поддерживает Matternet для расширения доставки дронами по всей …
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Description: /PRNewswire-PRWeb/ -- Three Sixty Seven Advisors is pleased to announce the successful transaction between Green Clean Commercial, a leading national provider...
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Searching for your content... Contact Us 11AM ET Sunday – 8PM ET Friday Mar 25, 2026, 11:15 ET Share this article Three Sixty Seven served as the exclusive advisor to SoftBank Robotics America for this transaction. TAMPA, Fla., March 25, 2026 /PRNewswire-PRWeb/ -- Three Sixty Seven Advisors is pleased to announce the successful transaction between Green Clean Commercial, a leading national provider of janitorial services, and SoftBank Robotics America. Three Sixty Seven served as the exclusive advisor to SoftBank Robotics America for this transaction. Brady Watkins, President at SoftBank Robotics America, stated: "This acquisition represents a significant strategic milestone for SoftBank Robotics America as we accelerate the integration of automation and robotics into the janitorial services sector. Establishing a strong operating platform was a critical first step in executing our long-term vision, and we were deliberate in selecting the right advisory partner to help us achieve that objective. We engaged Three Sixty Seven Advisors because of their recognized leadership within the middle-market facility services industry and their deep expertise in the commercial janitorial space. Their relationships, market insight, and disciplined execution were instrumental in identifying and securing the right platform for this initiative." Elliot Stipes, CEO at Green Clean Commercial, stated: "Smart Building X (SBX) represents an important strategic leap for the future of the company. The facility services industry is moving toward intelligent buildings, automation, and measurable outcomes. SBX allows us to deliver that future immediately, supported by advanced technology and aligned with a global innovation leader." Ryan Penna, Vice President at Three Sixty Seven, stated: "SoftBank Robotics America engaged Three Sixty Seven to identify and acquire a leading janitorial platform to anchor a transformative growth strategy; integrating SBRA's established automation and robotics capabilities into traditional facility services. This transaction represents a deliberate and forward-thinking move to redefine how technology and service delivery intersect within the industry. We are excited about the future of this partnership and look forward to seeing the meaningful growth, innovation, and industry impact this combination will generate. This transaction further reinforces Three Sixty Seven's role at the forefront of M&A across facility services." ABOUT SOFTBANK ROBOTICS AMERICA SoftBank Robotics America is the North American arm of SoftBank Robotics, driving technology forward by becoming a worldwide leader in robotics solutions. Headquartered in San Francisco, SoftBank Robotics America is a trusted partner and robot integrator that helps clients think beyond the technology, to incorporate the people and processes that solve the most pressing challenges and deliver best run operations. SoftBank Robotics America brings value and relevancy to senior living, hospitality, aviation, class A office space, multi-family, education, facilities management, and commercial cleaning. The goal is to develop a strong partnership and foundation for automation that will realize maximum strategic value on investment in robotics. ABOUT GREEN CLEAN COMMERCIAL Green Clean Commercial was founded in 2008, with the vision for transparency, trust in relationships, and successfully delivering results. Green Clean Commercial has consistently proven that people are at the heart of a great operation and when combined with innovative leading-edge technology, Green Clean Commercial makes it more efficient and effective. These winning principles have enabled GCC to expand nationally, serving public and private sectors including Fortune 500 and 100 clients ABOUT THREE SIXTY SEVEN ADVISORS Three Sixty Seven Advisors is a middle-market mergers and acquisitions advisory firm that leverages its industry experience and depth of relationships to assist their clients in helping realize the best outcome in every transaction. Its team of accomplished professionals has experience working across a wide array of industry verticals, creating a broad range of perspectives and viewpoints, which has helped deliver the top results for clients. Services provided by Three Sixty Seven include sell-side mergers & acquisitions, buy-side mergers & acquisitions, and corporate debt advisory for middle market companies & sponsors throughout the US from their headquarters in Tampa, FL. Media Contact Ryan Penna, Three Sixty Seven Advisors, 1 (516) 582-1046, [email protected], www.threesixtyseven.com SOURCE Three Sixty Seven Advisors Do not sell or share my personal information:
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Agility Robotics podnosi poprzeczkę. Zobacz, jak robot Digit dźwiga ciężary
Description: Dla człowieka 29 kg to nie jest jakiś wielki ciężar. Jasne, to zależy też od jednostki, ale większość z nas raczej nie miałaby problemu, gdyby musiała
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Humanoid przeszedł rygorystyczny test siły, wykonując martwy ciąg z obciążeniem wynoszącym 65 funtów (około 29 kilogramów). To było prawdziwe wyzwanie konstrukcyjne i programistyczne. Siła siłowników to jedno, ale kluczową rolę odegrała tutaj zaawansowana koordynacja całego „ciała”, która pozwoliła maszynie dynamicznie reagować na zmiany środka ciężkości. Martwy ciąg został wybrany przez inżynierów z Oregonu nie bez powodu. To ćwiczenie, które w świecie biologii angażuje niemal każdą grupę mięśniową, a w świecie robotyki wymaga perfekcyjnej synchronizacji ramion, nóg i tułowia. Każdy centymetr ruchu w górę to tysiące kalkulacji w czasie rzeczywistym, mających na celu utrzymanie stabilności i zapobieżenie wywróceniu się maszyny. Sekret sukcesu Digita tkwi w procesie szkolenia opartym na zaawansowanych symulacjach. Zamiast ryzykować uszkodzenie drogiego sprzętu w laboratorium, inżynierowie najpierw trenują „mózg” robota w środowisku wirtualnym. Tam cyfrowy odpowiednik Digita tysiące razy podnosi wirtualne ciężary o różnej masie i rozkładzie środka ciężkości. Dzięki temu system uczy się, jak korygować postawę, jak mocno zacisnąć chwytaki i jak balansować torsem jeszcze zanim realny robot dotknie fizycznego obciążenia. Pozwala to na odejście od sztywnego, ręcznego programowania ruchów na rzecz elastycznych zasad, pozwalających maszynie „czuć” ciężar i reagować na niego w sposób naturalny. To maszyna zaprojektowana do ciężkiej, powtarzalnej pracy w warunkach przemysłowych. Najnowsza iteracja robota przynosi szereg usprawnień, które mają uczynić go niezastąpionym elementem nowoczesnej logistyki: W wizji Agility Robotics, Digit ma uzupełniać autonomiczne wózki transportowe (AMR). Podczas gdy wózki zajmują się przewożeniem towarów na duże odległości, Digit przejmuje najbardziej precyzyjne zadania: zdejmowanie paczek z półek, układanie ich w stosy czy ładowanie wózków. Całość jest monitorowana przez platformę chmurową Arc, która pozwala zarządzać całą flotą robotów z jednego miejsca, dbając o ich konserwację i koordynację zadań. Czytaj też: Robotyczne mrówki z Harvardu. Budują i burzą bez planu, polegając na instynkcie otoczenia Pokaz siły Digita to jasny sygnał, że Chińczycy nie mają monopolu na robienie wrażenia w branży robotyki. Agility Robotics położyło właśnie kolejną cegiełkę pod przyszłość, w której to roboty będą wykonywać ciężkie prace w fabrykach, pozwalając ludziom skupić się na rzeczach mniej obciążających ich ciała. Oczywiście jak to zwykle bywa, na razie mieliśmy do czynienia z demonstracją w ściśle kontrolowanych warunkach, więc minie jeszcze trochę czasu, zanim komercyjne maszyny staną się równie silne. Źródło: Agility Robotics Portal technologiczny z ponad 29-letnią historią, piszący o nauce i technice, smartfonach, motoryzacji, fotografii. Technologie mamy we krwi!
Description: Figure AI, startup nel campo della robotica umanoide su cui ha investito anche OpenAI, ha svelato il suo ultimo progetto, Figure 02. (ANSA)
Description: Vider un lave-vaisselle, ranger les assiettes, et recommencer. Rien de spectaculaire, et pourtant : c’est précisément par là que Figure AI veut convaincre avec Figure 03, son robot humanoïde de troisième génération. Pensé pour s’attaquer aux corvées du quotidien, l'humanoïde mise sur une nouvelle IA maison, Helix 02, et sur une conception taillée pour la production en série.
Description: Eine Zukunft, in der Menschen und humanoide Roboter Seite an Seite leben, rückt immer näher in den Bereich des Möglichen. Tesla hat nun die nächste Generati...
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Sony AI table tennis robot outplays elite human players - …
Ace rotates its paddle as it prepares to return the ball back to its human opponent, Yamato Kawamata, during a match in December 2025. Credit: Sony AI. In an article published today in Nature, Sony AI introduce Ace, the first robot to beat elite human players in competitive physical sport. Although AI systems have shown advanced performance in digital domains and board games (such as complex video games, chess and Go), translating this to physical performance has remained a significant challenge. Such a feat requires perception, planning, and control to work in a high-speed domain on the scale of milliseconds. Table tennis is a demanding and complex real-world test for robotics, requiring rapid decision-making, precise physical execution, and continuous adaptation to an unpredictable opponent. The ball’s high speed, spin, and complex trajectories are central to competitive play. Director of Sony AI in Zürich, and project lead for Ace, Peter Dürr said “this research has shown that an autonomous robot can, in fact, win at a competitive sport, matching or exceeding the reaction time and decision making of humans in a physical space. Table tennis is a game of enormous complexity that requires split-second decisions as well as speed and power. This research breakthrough highlights the potential of physical AI agents to perform real-time interactive tasks, and represents a significant step toward creating robots with broader applications in fast, precise, and real-time human interactions.” A complete view of table tennis robot, Ace, including arm and track. Credit: Sony AI. Ace combines event-based vision sensors and a control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. Ace was designed with three novel components: Members of the Ace research team and table tennis officials pose with the robot and its human opponent, Mayuka Taira, following an official match in December 2025. https://robohub.org/wp-content/uploads/2026/04/Video-4_Sony-AI_Ace-Net-Bounce-Trajectory.mp4From Figure 4 in the Nature manuscript “Outplaying elite table tennis players with an autonomous robot” this film shows the robot making a split section change to its trajectory when the ball hits the net. Credit: Sony AI and Nature. For the results reported in the Nature publication, Ace was evaluated in matches against five elite players and two professional table tennis players, under International Table Tennis Federation (ITTF) regulations. Ace achieved three victories in five matches against the elite players, along with competitive performances in the remaining matches. There were some interesting results from the evaluations, including the fact that Ace was able to return a wide range of spins, consistently achieving over 75% return rate up to spins of 450 rad/s. The control systems behind Ace also allowed for quick reaction to unusual shots, such as balls bouncing off the net. This behavior illustrates the ability of the approach to generalize to situations that are both rare and hard to model in simulation. Following submission of the Nature manuscript, the team conducted additional competitive matches in December 2025 and March 2026, beating professional players in the process. Compared with earlier evaluations, Ace demonstrated higher shot speeds, more aggressive placement closer to the table edge, and faster-paced rallies, reflecting continued performance gains under competitive conditions. Find out more about the project in this video from Sony AI.
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AWS and Neura Robotics Team Up to Close Physical AI's …
Description: Amazon Web Services (AWS) and Neura Robotics announced a strategic partnership at Hannover Messe aimed at taking Physical AI from development into global
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Amazon Web Services (AWS) and Neura Robotics announced a strategic partnership at Hannover Messe aimed at taking Physical AI from development into global deployment. AWS serves as the primary cloud provider for the German robotics company and hosts the Neuraverse platform — the infrastructure for AI training, real-time data processing, and shared intelligence across robot fleets. The collaboration connects three areas: cloud infrastructure, AI development, and real-world validation in Amazon fulfillment centers. The Neura Gym training environments will be integrated with Amazon SageMaker to accelerate AI training pipelines. In parallel, Neura is joining the AWS Partner Network and jointly opening up new go-to-market activities for cognitive robotics solutions. Amazon is simultaneously evaluating the deployment of Neura robotic systems in selected logistics centers. The partnership addresses one of the most critical problems in Physical AI: while large language models have access to trillions of data points from the internet, robots have only a fraction of that. The collaboration targets the central challenge of Physical AI directly: the lack of training data. Robots that are meant to perceive, think, and act in the real world require continuous learning loops between simulation and reality. Neura’s Intelligence Layer enables robots to adapt and reliably collaborate with humans. Combined with AWS’s global cloud infrastructure, this creates the full stack to scale Physical AI rapidly. The Neuraverse platform on AWS establishes the foundation to train, test, and continuously improve robotic intelligence across customer, partner, and internal use cases. As the world’s leading cloud provider, AWS brings not only computing power but a comprehensive portfolio of AI and machine learning services to the partnership. For Neura, this means faster, more efficient, and reproducible AI training across platforms and fleets. AWS was chosen for its unmatched compute availability and managed service networks that translate Physical AI from theory into practice. “Physical AI will only reach its full potential if intelligence can be trained, validated, and continuously improved in the real world. With AWS, we gain the infrastructure to scale the Neuraverse globally. With Amazon, we have the opportunity to bring Physical AI into one of the most advanced operational environments in the world. This is how Physical AI moves from vision to global reality – from Europe, together for the world,” said David Reger, CEO and founder of Neura Robotics. Jason Bennett, VP and Global Head of Startups and Venture Capital at AWS, adds: “Neura represents exactly the kind of transformative thinking required to unlock the full potential of Physical AI. Their open platform approach addresses the industry’s most critical challenge–the data gap–and we’re excited to support their mission with AWS’s scalable cloud infrastructure. As Neura scales production, AWS will provide the reliable, global foundation needed to power the Neuraverse and enable real-time intelligence sharing across their entire fleet.” The AWS partnership marks another milestone in Neura’s growing ecosystem of global technology partners — encompassing cloud infrastructure, AI, semiconductors, and industrial deployment. These include Kawasaki as well as industry giants such as Schaeffler, Bosch, and Qualcomm Technologies. The shared goal: to enable millions of cognitive robots by 2030. Aus Datenschutz-Gründen ist dieser Inhalt ausgeblendet. Die Einbettung von externen Inhalten kann in den Datenschutz-Einstellungen aktiviert werden: Datenschutz-Einstellungen
