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| The multimodal leap: Engineering human-like intelligence into humanoid systems | https://timesofindia.indiatimes.com/blo… | 1 | Mar 28, 2026 16:00 | active | |
The multimodal leap: Engineering human-like intelligence into humanoid systemsDescription: Humanoid robots look convincing on stage or curated social media forwards. They walk, pick up objects, and in some demonstrations, they even smile and converse. This creates the expectation that machines will soon behave like... Content:
We encourage you to review our Terms of Service, and Privacy Policy. By continuing, you agree to the Terms listed here. In case you want to opt out, please click "Do Not Sell or Share My Personal Information" link in the footer of this page. We won't sell or share your personal information to inform the ads you see. You may still see interest-based ads if your information is sold or shared by other companies or was sold or shared previously. Interested in blogging for timesofindia.com? We will be happy to have you on board as a blogger, if you have the knack for writing. Just drop in a mail at toiblogs@timesinternet.in with a brief bio and we will get in touch with you. Somjit Amrit, stumbled upon the world of blogging. Reading is something he enjoys, a sort of second nature to him. The constructive corollary of reading is writing. A decade ago, he started writing reviews of the books, read. Subsequently, he started blogging on a variety of subjects: lessons learned from Mother Nature, first-hand experiences of relevance to help others, and technology for business are some of the areas of his interest. With over 30 years of rich professional experience, having led global business units spanning 4 continents, in the IT Services industry, he is the CEO of IIT Mandi iHUB and HCI Foundation which is one of the 25 Technology Innovation Hubs in the country sponsored by the Government of India. He is an engineer by qualification with a management degree from IIM Lucknow. He enjoys setting the “rake-through-the-hair” questions for quiz competitions. He is attempting to perfect a recently discovered natural talent in musical whistling as a stress buster. Can he pick up the harmonica this year as the grand resolution for the year? He will surely update you. LESS ... MORE Humanoid robots look convincing on stage or curated social media forwards. They walk, pick up objects, and in some demonstrations, they even smile and converse. This creates the expectation that machines will soon behave like humans. In practice, however, most humanoid platforms excel at isolated capabilities but struggle in continuous, unscripted social and physical interaction. They may drop objects, misinterpret gestures, mis-time responses, or pause when faced with noisy sensory input. These limitations reveal a deeper truth: building a humanoid robot is not about perfecting any single component. It is about closing tightly coupled loops between perception, reasoning, and action across multiple modalities. Multimodality is the structural solution to this problem. Human interaction is a tightly coupled stream of audio, visual, tactile and contextual signals that arrive and must be interpreted together in real time. For rigid robots to behave with human-like fluidity, their software stacks cannot treat these channels as separate pipelines that exchange occasional messages. Instead, they must build shared internal representations that are synchronized in time, fused across sensing modalities, and available both to perception modules that infer intent and to control modules that plan and execute motion. When a person points while saying, “Put it there,” the robot should align the gesture, the pointing vector, the spoken phrase, the gaze and the scene geometry in a single moment of understanding, and then generate a motor plan that respects force constraints, spatial and temporal balance, and the social context of the interaction. The Missing Link: Synchronization and Real-Time Fusion While multimodality provides the structural foundation, the real challenge lies in synchronizing and fusing these multiple sensory streams. Humanoid robots cannot achieve human-level fluency by processing visual, auditory, tactile, and contextual information independently. Each modality informs and constrains the others, and seamless integration in real time is essential for coherent decision-making. Key capabilities enabled by multimodal AI include: Synthesize Context: A robot interacting with a human, needs to combine facial expression data, speech audio, and environmental context to determine whether the person is frustrated, joking, or requesting urgent help. Adaptive Interaction: By fusing tactile feedback (object weight, texture) with visual input (object shape, location), a robot can dynamically adjust its grip or trajectory without pre-programming every possible scenario. Predictive Coordination: Multimodal fusion allows anticipatory action. For example, combining gaze tracking with speech patterns can enable the robot to act on intentions before they are explicitly verbalised. Developing these capabilities requires end-to-end multimodal neural networks that mirror human cognitive processes. Latent representations must encode cross-modal dependencies and be updated continuously to allow smooth, safe, and intelligent interaction. Without this real-time integration, humanoid robots would continue to operate with limited agility (or pronounced rigidity) and constrained social responsiveness (or visible gawkiness), regardless of how advanced their individual sensors or algorithms may be. Moving Forward: Towards Agile, Context-Aware Human-like Robots The future of AI is not merely automation; it is augmentation and interaction. To build more agile and context-aware humanoid robots, research efforts should focus on: Robust Data Fusion Techniques: Developing algorithms that fuse asynchronous, multi-sensory data into unified latent representations, rather than merely combining outputs from separate modules. Contextual Understanding Engines: Creating AI that can interpret intent, social nuance, and environmental context, enabling reliable operation in unpredictable, real-world environments. Ethical and Responsible AI: Ensuring that multimodal systems respect privacy, avoid bias, and interact safely, particularly as they begin to operate in sensitive human contexts. The current limitations of humanoid robots are not failures; they are building blocks. By investing in multimodal AI research and the Technology Innovation Hub at IIT Mandi, we are laying the foundation for fluidic humanoid robots (human-like robots) that redefine our relationship with machines. The ultimate goal is a future where the line between physical and digital, human and AI, becomes seamless. Robots will not merely act; they will perceive, reason, and interact in ways that are coherent, context-aware, and profoundly human-like while abiding with the overarching aspect of responsible and ethical AI. Views expressed above are the author's own. High desibels The Vance Dance OMG! Don’t bet on bans The 65 Lakh Question ‘Centrism isn’t nostalgia, it is survival’ So, who’s straying? Beyond the noise Rethinking stray dogs: From crisis to opportunity Swadeshi Diwali Down the drain Interested in blogging for timesofindia.com? We will be happy to have you on board as a blogger, if you have the knack for writing. Just drop in a mail at toiblogs@timesinternet.in with a brief bio and we will get in touch with you. TOI Edit Page,Voices Erratica,TOI Edit Page,Tracking Indian Communities Juggle-Bandhi,TOI Edit Page TOI Edit Page
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| Universal Robots and Scale AI launch the UR AI Trainer | https://thenextweb.com/news/universal-r… | 1 | Mar 28, 2026 16:00 | active | |
Universal Robots and Scale AI launch the UR AI TrainerURL: https://thenextweb.com/news/universal-robots-and-scale-ai-launch-the-ur-ai-trainer Description: Universal Robots and Scale AI launch the UR AI Trainer at GTC 2026, a leader-follower system that captures force and visual data to train AI models. Content:
Revealed at GTC 2026, the leader-follower imitation learning platform captures force, motion, and visual data directly on production hardware, closing the gap between AI research labs and factory floors. Universal Robots has launched the UR AI Trainer, a hardware-software system built in collaboration with Scale AI that allows operators to generate high-fidelity robot training data directly on the same cobots they deploy in production. Announced at NVIDIA’s GTC 2026 conference in San Jose on 16 March, the system is designed to close what the robotics industry calls the lab-to-factory gap: the practical difficulty of moving AI models trained in controlled research settings into real-world manufacturing environments. The core mechanism is a leader-follower setup. A human operator physically guides a leader robot through a task, say, packaging a smartphone, while a follower robot mirrors the motion in real time. Throughout each demonstration, the system simultaneously captures motion trajectories, force feedback data, and visual information, producing the structured multimodal datasets needed to train Vision-Language-Action models. The key differentiator is that this happens on the same industrial cobots UR sells into production: training data collected on a UR3e or UR7e in a controlled AI training cell can be used to train models that then run on identical hardware in a factory. TNW City Coworking space - Where your best work happens A workspace designed for growth, collaboration, and endless networking opportunities in the heart of tech. “Our customers, ranging from large enterprises to AI research labs, are no longer just asking for AI features. They need a way to collect high-fidelity, synchronized robot and vision data to train AI models on the same robots they intend to deploy. Our AI Trainer is the industry’s first direct lab-to-factory solution for AI model training.” – Anders Beck, VP of AI Robotics Products, Universal Robots Most robot training data today is collected on research platforms using vision alone. That approach works for tasks where position is sufficient, but fails for anything involving delicate contact, screwing, pressing, inserting, or any manipulation where the robot needs to respond to resistance. Universal Robots argues that its Direct Torque Control and force feedback capabilities give the AI Trainer a physical fidelity advantage: the robot can not only learn what to do visually but also how it should feel to do it correctly. This matters particularly for the category of tasks the robotics research community describes as contact-rich manipulation, assembly operations where parts must fit together with precision and the robot must adjust its grip in response to what it encounters. Those tasks have historically been among the hardest to automate reliably, and they represent a significant share of the manufacturing operations that remain human-dependent. The UR AI Trainer deploys on UR’s AI Accelerator platform and integrates Scale AI’s software stack to capture, structure, and manage the training data generated during demonstrations. The collaboration is explicitly framed as a flywheel: operators collect demonstration data, models are trained on that data, deployed robots improve performance, and the improved performance feeds back into the next round of training. “Universal Robots is a leader in industrial robotics, and its global footprint offers the ideal foundation for data capture and AI deployment. Together, we’ve created an integrated robotics data flywheel, allowing customers to train, deploy, and improve their AI models faster than ever before.” – Ben Levin, General Manager, Physical AI, Scale AI As part of the collaboration, Universal Robots and Scale AI will release a large-scale industrial dataset collected on UR robots later in 2026. The GTC demo captures this pipeline in miniature: visitors at UR’s booth can guide two UR3e leader robots through a smartphone packaging task, with the demonstration data recorded in real time on Scale’s stack and immediately replayable on the AI Trainer. A parallel virtual demo, built in NVIDIA Omniverse using Isaac Sim, shows the same task being trained synthetically using two Haply Inverse3 haptic devices, demonstrating the simulation-to-real pathway alongside the physical data collection. Accompanying the AI Trainer launch is the first public demonstration of Generalist AI’s embodied foundation models. Generalist was founded by Pete Florence, a former Senior Research Scientist at Google DeepMind whose prior work includes co-authorship on RT-2 (Robotic Transformer 2) and PaLM-E, alongside Andy Zeng and Andy Barry, both former colleagues at DeepMind and MIT. The startup, which counts NVIDIA’s venture arm NVentures among its investors, emerged from stealth at GTC 2025 and has since been developing what it describes as embodied foundation models for general-purpose robot dexterity. At GTC 2026, two UR7e robots running Generalist’s model autonomously execute the same smartphone packaging task that the AI Trainer demos use for human-guided data collection. The demonstration is designed to show the end state that the training pipeline is building towards: robots that can complete contact-rich manipulation tasks reliably and without pre-programmed trajectories. “Generalist is building embodied foundation models that deliver industry-leading dexterity and reliability. This demonstration on Universal Robots’ trusted industrial platform shows how physical commonsense can be translated into real-world capability, paving the way for deployment across industries at scale.” – Pete Florence, co-founder and CEO, Generalist AI Universal Robots frames the industrial scale of its installed base, over 100,000 cobots deployed worldwide, as a structural advantage in the race to build physical AI. The argument is that the quality of an AI model depends heavily on the quality and quantity of the training data, and that UR’s fleet of production robots represents the largest potential source of real-world manipulation data in the industry. The AI Trainer is the mechanism for unlocking that data. NVIDIA’s physical AI ecosystem surrounds the launch: the company is also exploring use of the NVIDIA Physical AI Data Factory Blueprint to automate synthetic data generation, complementing the physical demonstration data. “The shift toward Physical AI requires a fundamental move from rigid, pre-programmed automation to generalist robots that can perceive, reason, and learn through human-like interaction. By leveraging the NVIDIA Isaac simulation frameworks, Universal Robots is building a scalable engine for high-fidelity data capture and generation, providing the essential infrastructure to train the next generation of autonomous systems at scale.” – Amit Goel, Head of Robotics and Edge AI Ecosystem, NVIDIA Universal Robots is a subsidiary of Teradyne Robotics, itself a division of Teradyne (NASDAQ: TER). The GTC 2026 announcement comes at a moment when physical AI, the application of AI techniques to real-world robotic manipulation, has attracted significant attention and investment, driven partly by the success of large language models and the argument that similar scaling approaches can work for robot learning given sufficient high-quality data. I am the Editor in Chief for TNW, covering technology not as a parade of launches and valuations, but as a system of influence, persuasion, (show all) I am the Editor in Chief for TNW, covering technology not as a parade of launches and valuations, but as a system of influence, persuasion, and change. I write about startups, venture capital, digital policy, and Europe ecosystem, with an eye on the larger story beneath them: who gets to build the future, who profits from it, and how Europe is learning to speak in a louder voice of its own. Before moving into senior editorial leadership, I've built my career for over +10 years across journalism, storytelling, content strategy, SEO, and digital publishing, with experience in SaaS, hospitality, art, and culture. Get the most important tech news in your inbox each week. The heart of tech A Tekpon Company Copyright © 2006—2026, Cogneve, INC. Made with <3 in Amsterdam.
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| Google DeepMind teams up with Agile Robots for AI robotics … | https://seekingalpha.com/news/4568275-g… | 0 | Mar 28, 2026 16:00 | active | |
Google DeepMind teams up with Agile Robots for AI robotics push (GOOG:NASDAQ)Description: Google DeepMind partners with Agile Robots to advance AI-driven robotics using Gemini Robotics models, boosting deployment and training. Content: |
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| Unitree targets IPO after revenue quadruples on robotics demand | https://kr-asia.com/unitree-targets-ipo… | 1 | Mar 28, 2026 00:00 | active | |
Unitree targets IPO after revenue quadruples on robotics demandURL: https://kr-asia.com/unitree-targets-ipo-after-revenue-quadruples-on-robotics-demand Description: The Chinese startup has filed for a listing on Shanghai’s STAR Market. Content:
Written by T. K. Lin Published on 24 Mar 2026 2 mins read Unitree Robotics has filed for an IPO on the Shanghai Stock Exchange, seeking to raise up to RMB 4.2 billion (USD 609 million). According to the company’s prospectus, about half of the proceeds will be allocated to R&D in robotics and embodied intelligence. The remaining funds are set to support product development and manufacturing facilities following the IPO. The review process is expected to begin soon, with Unitree aiming to list on the STAR Market, which typically caters to technology and tech-focused companies. The planned IPO follows strong sales growth. According to the prospectus, Unitree’s sales reached RMB 1.7 billion (USD 246.5 million) in 2025, about 4.3 times higher than the previous year, while net profit tripled to about RMB 287.6 million (USD 41.7 million). Notably, revenue from humanoid robots has risen sharply in recent years, alongside broad-based growth across its product categories, based on data disclosed in the filing: frameborder="0" scrolling="no" sandbox="allow-same-origin allow-forms allow-scripts allow-downloads allow-popups allow-popups-to-escape-sandbox allow-top-navigation-by-user-activation"> Even before reaching these financial milestones, Unitree had long been viewed as one of China’s leaders in robotics since its founding in 2016. That reputation stems not only from the company’s high-profile demonstrations and public showcases, but also from its founder, Wang Xingxing. Wang has been vocal about the need for embodied intelligence to advance through both hardware and software, rather than placing too much emphasis on hardware alone. In previous conversations with 36Kr, he said Unitree has a sizable team focused on artificial intelligence model development, including models that power its robots. According to Wang, the company’s efforts extend beyond motion control, where it is already considered strong, into more advanced capabilities such as cognition and planning. Among Unitree’s latest offerings is the H2, a full-sized humanoid robot, though the G1 is likely more familiar to the public. The G1 has been used in public showcases, including a martial arts segment during this year’s Lunar New Year gala in China, where 24 G1 robots were deployed to “spar” with human performers and execute movements such as table vaults and backflips. Unitree also participated in last year’s broadcast. height="278" frameborder="0" allowfullscreen="allowfullscreen" data-ytbridge="vidSurrogate2"> Beyond humanoid robots, Unitree also develops other robotic form factors, including quadruped machines such as the Go2 robot dog. In terms of commercialization, Unitree is among the frontrunners, projecting shipments of 10,000–20,000 units in 2026. Its domestic competitors include UBTech Robotics, which develops the Walker line of humanoid robots, as well as broader technology players such as Xpeng, which is developing the Iron humanoid robot. Globally, Unitree is often compared with Tesla, which is developing the Optimus robot. Note: RMB figures are converted to USD at rates of RMB 6.90 = USD 1 based on estimates as of March 24, 2026, unless otherwise stated. USD conversions are presented for ease of reference and may not fully match prevailing exchange rates. Loading... Subscribe to our newsletters KrASIA A digital media company reporting on China's tech and business pulse.
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| Beyond kung fu: Unitree launching humanoid robot for household chores … | https://www.notebookcheck.net/Beyond-ku… | 1 | Mar 28, 2026 00:00 | active | |
Beyond kung fu: Unitree launching humanoid robot for household chores and elder care - NotebookCheck.net NewsDescription: The humanoid robot company that taught them to do backflips, march en masse, and do kung fu choreography might soon be having them do laundry. Unitree aims for more practical applications with a low-cost humanoid for household purposes. Content:
While Elon Musk is of the opinion that the Tesla Optimus robot will be better than its Chinese competition when it actually launches, companies like Unitree have already shipped thousands of humanoid robots and are moving from the flashy demo stage to developing household helpers. Unitree sold more than 5,500 humanoid robots in 2025, surpassing the combined output of all U.S. competitors, including Tesla, Figure AI, and Agility Robotics, and is aiming to ship up to 20,000 units in 2026. The company is clearly not waiting for the technology to be perfect before scaling. Better known for viral clips of a marching robot army or the majestic martial arts performance of its WuBots stealing the Spring Festival Gala spotlight, it is now planning a more domestically applicable future for its machines. According to Unitree's Shanghai Stock Exchange IPO filing with a proposed fundraising target of more than $600 million, the company will be launching a cheaper "general-purpose humanoid robot embodied foundation model" by 2030. The model is described as covering four core generalization pillars: scene, instruction, action, and task. It is designed to close the loop between cloud-based model training, edge-side inference, and real-world data collection. This is the kind of autonomous system that currently powers self-driving EVs but is repurposed for humanoid robot decision-making and execution. While the near-term focus for humanoid robots like Optimus or Hyundai's Atlas has been industrial and manufacturing environments, where conditions are controlled enough for today's models to operate reliably, Unitree aims to start selling a general-purpose humanoid within the next three years. As generalization, reliability, and safety mature, says Unitree, the application domain will expand from vertical industrial scenarios into household services, elder care, and daily living, like doing laundry. That ambition is not purely theoretical, as Unitree's R1 robot is already capable of voice- and vision-based multimodal interaction for simple household tasks, and its open-sourced UnifoLM-VLA-0 model allows the G1 humanoid to autonomously handle 12 different categories of complex manipulation using a single policy. It can unpack a tennis racket on its own, for instance, not just memorize preset kung fu choreography. The race now is to accumulate real-world interaction data, as industry experts argue that when a certain threshold is reached, the general intelligence will rise significantly, marking the point when humanoid robots will truly be ready to move from the stage to the home at a much lower cost than today's units. Get the Unitree Go2 robot dog quadruped on Amazon IT Home
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| Reachy Mini Hits AliExpress, Taking Embodied AI Global - Money … | https://moneycompass.com.my/reachy-mini… | 1 | Mar 25, 2026 08:00 | active | |
Reachy Mini Hits AliExpress, Taking Embodied AI Global - Money CompassURL: https://moneycompass.com.my/reachy-mini-hits-aliexpress-taking-embodied-ai-global/ Description: Money Compass is one of the credible Chinese and English financial media in Malaysia with strong influence in Malaysia’s financial industry. As the winner of the SME Award in Malaysia for 5 consecutive years, we persistently propel the financial industry towards a mutually beneficial framework. Since 2004, with the dedication to advocating the public to practice financial planning in everyday life, Money Compass has accumulated a vast connection in ASEAN financial industries and garnered government agencies and corporate resources. At present, Money Compass is adjusting its pace to transform into Money Compass 2.0. Consolidating the existing connections and network, Money Compass Integrated Media Platform is founded, which is well grounded in Malaysia whilst serving the ASEAN region. The mission of the new Money Compass Integrated Media Platform is to become the financial freedom gateway to assist internet users enhance financial intelligence, create wealth opportunities and achieve financial freedom for everyone! Content:
Developer-favorite desktop robot now available to more consumers worldwide through AliExpress Anniversary Sale LOS ANGELES, March 17, 2026 /PRNewswire/ — Reachy Mini, the open-source desktop robot that has captivated the developer community, is now available to global consumers on AliExpress, coinciding with the platform’s Anniversary Sale promotion. This extension marks an important step in expanding Reachy Mini beyond the open-source developer community to a broader consumer audience across key markets including the United States, Europe, South Korea, Japan and Brazil. Developed within the Hugging Face ecosystem, a leading open-source AI platform, Reachy Mini is a flagship embodied AI project designed for human-AI interaction, creative coding, and hands-on experimentation. Reachy Mini has earned strong recognition among AI researchers and developers worldwide. Now, through AliExpress, the robot is becoming more broadly accessible to consumers interested in exploring the next wave of AI-powered robotics. The addition of Reachy Mini reflects AliExpress’ broader push to attract innovative technology products and brands. Seeed Studio, the hardware manufacturing partner behind Reachy Mini, selected AliExpress as its consumer market launchpad. “AliExpress has made a strong commitment to supporting premium hard tech products, which aligns perfectly with Seeed Studio’s mission,” said Joey Jiang, VP of Global Sales and Marketing at Seeed Studio. “As an open-source hardware provider focused on emerging technologies, we have built a technology ecosystem based on open-source hardware, AI-driven capabilities, and cross-domain co-creation. The Anniversary Sale gives us a rare opportunity to tap into global demand and introduce Reachy Mini to a wider audience.” The timing comes amid growing visibility for Reachy Mini. At CES 2026, NVIDIA CEO Jensen Huang featured Reachy Mini in his keynote address, demonstrating the desktop robot to a global audience and signaling that the era of embodied AI has arrived. The spotlight from one of the world’s most-watched tech stages has helped drive surging interest and pre-order demand for the compact robot. From March 16 through March 25, Reachy Mini will be available in stock – no pre-orders required. Seeed Studio’s official AliExpress store will release limited units daily throughout the promotional period, giving consumers worldwide a chance to own one of the most talked-about robots in the embodied AI space. Reachy Mini Robot Reachy Mini is among a growing roster of premium tech brands joining AliExpress Brand+, the platform’s dedicated channel for high-end global technology brands. In 2025, Unitree Robotics opened its official flagship store on AliExpress, while Rokid AR glasses and other emerging global tech brands have used the platform to reach international consumers directly. As more frontier technology brands seek global scale, AliExpress is emerging as the platform of choice for hard tech brands looking to grow beyond their home markets. “Reachy Mini joining AliExpress during our Anniversary Sale is a strong signal of where consumer technology is headed,” said Chris Carl, Head of Marketing, AliExpress U.S. “We aim to bring the world’s most innovative technology products to consumers everywhere.” Reachy Mini is available HERE through Seeed Studio’s official AliExpress store. About AliExpress Launched in 2010, AliExpress is a global e-commerce platform dedicated to creating a better shopping experience for hundreds of millions of consumers in more than 200 countries and regions. In addition to the English version, the AliExpress platform is available in 15 other languages. AliExpress is part of Alibaba International Digital Commerce Group. Your email address will not be published. Required fields are marked * Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved Login to your account below Remember Me Please enter your username or email address to reset your password. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved
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| Embodied AI turned into growth driver | http://www.ecns.cn/news/sci-tech/2026-0… | 1 | Mar 25, 2026 08:00 | active | |
Embodied AI turned into growth driverURL: http://www.ecns.cn/news/sci-tech/2026-03-21/detail-ihfaunkv7716550.shtml Content:
Xi stressed importance of future industries for boosting new quality productive forces Inside a humming factory in Hefei, Anhui province, a 1.66-meter-tall humanoid robot dubbed Lingshu steadies its grip, pivots and places a wafer-thin semiconductor onto a moving line, an extremely high precision task where an aberration of just a fraction of a millimeter could spell failure. The scene, once confined to research labs, is now moving rapidly onto Chinese factory floors, offering a glimpse into China's efforts to develop future industries using embodied artificial intelligence as a strategic pillar of economic growth. During this year's first group study session of the Political Bureau of the Communist Party of China Central Committee in January, President Xi Jinping emphasized that cultivating future industries is of great significance for developing new quality productive forces, building a modernized industrial system, improving people's quality of life, and promoting people's well-rounded development and all-around social progress. In February, Xi, who is also general secretary of the CPC Central Committee, visited Beijing's E-Town â a hotbed for emerging and future industries, where he took a look at a national information technology innovation park, once again turning his attention to representative scitech innovations. China's national policy signals are reinforcing the momentum. The 2026 Government Work Report pledged to establish mechanisms to boost investment in future industries, including embodied AI, future energy, quantum technology, brain-computer interface and 6G technology. It marked a renewed effort to develop embodied AI after the concept was first elevated as a national priority in 2025. Embodied AI refers to the integration of AI into physical systems, enabling them to interact with the physical world. Humanoid robots represent the most advanced form of embodied AI at the current stage. Yao Qizhi, a Turing Award winner and an academician at the Chinese Academy of Sciences, said, "Over the past five years, China has made rapid progress in embodied AI, especially humanoid robots, reaching the top international tier and even taking a leading position in some areas." Yao added that embodied AI represents a convergence of computing power, algorithms, hardware and real-world data. "For China, it is not a single technological breakthrough, but a systematic project," he said. Zhang Zhaohui, founder and CEO of Youibot, the developer of Lingshu, said that unlike traditional industrial robots working on programmed tasks, humanoid robots allow a single AI system to control different types of robotic bodies, which is more efficient. "Lingshu, for instance, has already been deployed in electronics factories and logistics centers in cities including Hefei, Suzhou in Jiangsu province and Chongqing," he said, adding that one such robot can work as efficiently as eight to 12 human workers per shift while operating 24 hours a day. Zhang said that the company is also testing tea picking using humanoid robots. As tea leaves are extremely fragile and grow in irregular patterns, robots would be required to identify and pick leaves with extremely high precision. Tangible results The Development Research Center of the State Council forecasts that the domestic embodied AI market could reach 400 billion yuan ($55 billion) by 2030 and surpass 1 trillion yuan by 2035, driving productivity gains across logistics, manufacturing and services. China's manufacturing scenarios, in particular, give embodied AI an edge. Early adopters are already reporting tangible results. At carmaker Nio's smart manufacturing plant, embodied AI technology is being used to navigate automated storage systems, retrieve parts and assemble vehicle bodies. Nio said the technology has boosted production efficiency by more than 30 percent, cut labor costs by 25 percent and reduced defect rates by 40 percent. Meanwhile, robot company UBTech's Walker S2 humanoid robots are being deployed in factories across South China. The company said it has secured orders worth over 100 million yuan and plans to deliver more than 1,000 units in 2026. Data from International Data Corporation show that global shipments of embodied AI industrial robots reached 18,000 units in 2025 and were expected to exceed 50,000 in 2026, with China accounting for more than 45 percent of the market. Public attention on embodied AI has also surged. During this year's Spring Festival Gala, humanoid robots performed complex stunts including flips, martial arts and synchronized group movements â a sharp leap from the simpler demonstrations seen just a year earlier. Li Lecheng, minister of Industry and Information Technology, said: "Such performances showcase more than entertainment. They reflect China's advances in translating AI into real-world applications, a vivid display of the country's growing innovation capacity." Hurdles, however, remain. Lin Yonghua, chief engineer at the Beijing Academy of Artificial Intelligence, said, "More efforts are needed to achieve stable, high-quality control of humanoid robots, improve dexterous manipulation capabilities and overcome constraints in power supply and heat management of the robots." Lin added that global competition is also intensifying with the United States, Japan and Germany ramping up investment in embodied AI. "For China, such efforts are also about securing industrial resilience and supply chains," she said. He Xiaopeng, CEO of electric vehicle maker Xpeng, called for greater national-level R&D funds and standardized frameworks â similar to autonomous driving classifications â to accelerate the commercialization of embodied AI. Chinese AI startup tops global embodied intelligence benchmark World's largest embodied AI data factory opens in Tianjin
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| realistic humanoid robots by aheadform mimic living people's skin | https://www.designboom.com/technology/r… | 1 | Mar 24, 2026 16:00 | active | |
realistic humanoid robots by aheadform mimic living people's skinDescription: aheadform develops realistic humanoid robots that mimic living people with their lifelike skin and moving faces and mouths. Content:
AheadForm develops realistic humanoid robots that mimic living people with their lifelike skin and moving faces and mouths. Powered by AI and mechanics, the company designs these human-like machines to replicate our emotions and behavior and to have the ability to learn from what they see and where they’re at using algorithms and degrees of freedom in their movement. AheadForm’s realistic humanoid robots come with custom-designed brushless micro motors installed in their facial areas; that’s why they can move their eyebrows, lips, and eyes to match their speeches and emotions. It produces very little noise, so it’s barely audible, and it fits in the machine’s heads. The company’s engineers developed their own control software that synchronizes the motor’s response with the robot’s AI, so each facial movement matches their spoken words or facial expressions. The robot’s head design includes moving eyes, eyelids, and a mouth that syncs with voice output, and the structure under the skin features mechanical parts connected to micro motors that pull or release at different angles to create lifelike expressions. These systems help AheadForm’s realistic humanoid robots understand human gestures, facial expressions, and tone. The AI system integrates language and visual models so that the machines can look at a person, recognize their emotional state from their facial expression, and respond with matching tone and language. It allows real-time learning, meaning the robots improve their replies as they interact more with the living people. all images courtesy of AheadForm AheadForm has two series for its realistic humanoid robots. In the company’s ELF series, the machines use up to 30 degrees of freedom, meaning that each joint or facial feature can move independently, allowing for a wide range of actions and expressions. There’s a control system that handles the movement of each motor, while the AI system helps the devices learn from their surroundings and adjust their behavior over time. They’re aware of their environment, like living people using sensors, processing what they see and hear and then responding in a way that fits the situation. The ELF V1 model in the series already shows this, and the company says that it can talk to people, understand what they’re saying and what they want it to do, and perform tasks. Then there’s the LAN series, a group of AheadForm’s realistic humanoid robots designed for movements like head turns, hand motions, and walking balance. They’re covered in a synthetic skin material that mimics the softness and texture of human skin, and this material can resist moisture and temperature changes even after repeated movements. The company says that it plans to use the Lan Series in roles that need more mobility and handling ability, such as guiding people, performing simple manual tasks, or demonstrating products. The robots are built using a combination of lightweight metal alloys, synthetic polymers, and silicone-based materials that are waterproof and flexible. They can stretch and return to their original shape and are attached over a mechanical skeleton that supports facial and body movements. So far, AheadForm released busts of these moving and talking realistic humanoid robots in 2025, hinting at its future plans to unveil more. even the facial features, such as hair, are lifelike these realistic humanoid robots by AheadForm replicate the living people’s skin texture detailed view of the machines’ skin and features these robots come with custom-designed brushless micro motors installed in their facial areas neck view of the machines so far, AheadForm released busts of these moving and talking realistic humanoid robots project info: name: Elf Series, Lan Series company: AheadForm
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| CATL successfully deploys humanoid robots to quality-critical work on EV … | https://www.gizmochina.com/2025/12/18/c… | 1 | Mar 24, 2026 16:00 | active | |
CATL successfully deploys humanoid robots to quality-critical work on EV batteries - GizmochinaDescription: Its main task involves attaching battery connectors, a job that demands high precision, consistency, and careful control of applied force. Content:
Humanoid robots have been a popular talking point in recent months, but most examples so far have involved carefully staged demos rather than real factory work. CATL, the worldâs largest EV battery maker, now claims it has crossed that gap, quietly rolling out humanoid robots on an actual production line. The company says it has completed large-scale deployment of its Moz humanoid robot at a battery pack factory, making it the first power battery production line to use humanoid âembodied intelligenceâ robots at scale. Moz was developed by Spirit AI, a CATL subsidiary focused on robotics and automation. Rather than handling simple pick-and-place jobs, Moz is reportedly positioned at quality-critical stages of the production process. Its main task involves attaching battery connectors, a job that demands high precision, consistency, and careful control of applied force. According to CATL, the robot has reached performance levels comparable to experienced human workers, delivering a reported 99% success rate in connector insertion. That level of reliability is achieved through an end-to-end vision system that allows the robot to adapt in real time. Moz can compensate for slight misalignments in materials or connection points by adjusting its posture and movements on the fly. It also monitors how much force it applies, ensuring wiring harnesses are secured firmly without damaging fragile components. CATL contrasts this with reports of difficulties faced by other humanoid robots during factory trials, including overheating joints and failures in complex mechanical assemblies. While those systems have drawn attention through public demonstrations, many have yet to prove they can operate continuously in demanding industrial environments. The timing is notable, as Chinaâs humanoid robotics sector is expanding rapidly, with some analysts already warning of potential overcapacity similar to what the country experienced in EV manufacturing. CATLâs deployment suggests that, at least in some cases, humanoid robots are moving beyond experiments and into practical, revenue-generating roles on the factory floor. Donât miss a thing! Join our Telegram community for instant updates and grab our free daily newsletter for the best tech stories! For more daily updates, please visit our News Section. (Source: CATL)
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| Learning To Play Tennis | https://www.i-programmer.info/news/169-… | 1 | Mar 24, 2026 00:01 | active | |
Learning To Play TennisURL: https://www.i-programmer.info/news/169-robotics/18743-learning-to-play-tennis.html Description: Programming book reviews, programming tutorials,programming news, C#, Ruby, Python,C, C++, PHP, Visual Basic, Computer book reviews, computer history, programming history, joomla, theory, spreadsheets and more. Content:
Training humanoid robots to do things seems to be the way to go compared to the "traditional" engineering approach. We can't tell a robot how to play tennis but we can let it learn by trial and error. As we have commented before, the age of the engineering approach to robotics is probably well and truly over. We no longer need to work out how to program the movements that are needed to get a job done - we can simply use reinforcement learning to get the robot to figure it out for itself. Of course, this isn't as simple as it sounds and it takes a lot of computing power - but we are seeing more and more examples of it in action and working well. The latest is a humanoid robot that can play tennis - well it has learned how to return the ball and keep a rally going. While it has no strategy for playing tennis and isn't trying to score points against its opponent, It is remarkable impressive and slightly spooky. The system is called LATENT Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa - which is about as contrived an acronym as can be invented! However, don't let this put you off as its an interesting and generalizable approach. The training data is imperfect motion capture - motion fragments that capture the primitive skills involved - forehand, backhand and footwork. It seems that it is possible to learn these fragments and put them together into a useful order and, with some additional reinforcement learning, the humanoid robot, an off-the-shelf Unitree G1, can master returning the ball using classic tennis strokes. This is all done in simulation and then, using a clever technique, is transferred to the real world robot. As the research paper says: "Our method achieves surprising results in the real world and can stably sustain multi-shot rallies with human players..." Yes I have to agree and it seems such a short time ago we were laughing at humanoid robots attempting to walk like humans... Latent ANYmal For Badminton Quadrupedal Parkour Unitree Robots Perform Kung Fu Display Meet Figure 03: Dishwasher-Loading Robot Humanoid Alpha Learns To Wrap Xmas Presents A World First For Humanoid Robots To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Facebook or Linkedin. To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Facebook or Linkedin. JetBrains Releases VS Code Java To Kotlin Converter03/03/2026JetBrains, creators and major supporters of Kotlin, has released an extension for Visual Studio Code that can be used to convert Java code to Kotlin. The extension uses LLMs to provide "idiomatic [ ... ] + Full Story XAML.io Adds Nuget Package Browser Support26/02/2026XAML.io has been updated to add support for Nuget packages directly in the browser, along with code sharing via URL. XAML.io is a free, browser-based XAML designer and C# editor built on top of t [ ... ] + Full StoryMore NewsGoogle Is Still On Target To Close AndroidDiscover the 2026 Dates For Google I/O and Microsoft BuildpgEdge MCP Server For PostgresApache NetBeans 29 Improves Git IntegrationBun Adds Parallel Script SupportApache Geode 2 ReleasedQuantum Information Science Pioneers Gain Turing AwardWing Python Improves Syntax HighlightingLearn SQL For Data Engineering - The CourseZvec - Lightweight Vector DatabaseFuture-Proof Your Career with AI Professional CertificateRust 1.94 Adds Array WindowsCommunity Asks Oracle For MySQL Foundation JetBrains, creators and major supporters of Kotlin, has released an extension for Visual Studio Code that can be used to convert Java code to Kotlin. The extension uses LLMs to provide "idiomatic [ ... ] JetBrains, creators and major supporters of Kotlin, has released an extension for Visual Studio Code that can be used to convert Java code to Kotlin. The extension uses LLMs to provide "idiomatic [ ... ] XAML.io has been updated to add support for Nuget packages directly in the browser, along with code sharing via URL. XAML.io is a free, browser-based XAML designer and C# editor built on top of t [ ... ] XAML.io has been updated to add support for Nuget packages directly in the browser, along with code sharing via URL. XAML.io is a free, browser-based XAML designer and C# editor built on top of t [ ... ] Comments Make a Comment or View Existing Comments Using Disqus or email your comment to: comments@i-programmer.info Make a Comment or View Existing Comments Using Disqus or email your comment to: comments@i-programmer.info
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| Model-based approaches and reinforcement learning for robust and autonomous locomotion … | https://theses.hal.science/tel-05559751… | 1 | Mar 24, 2026 00:01 | active | |
Model-based approaches and reinforcement learning for robust and autonomous locomotion of humanoid robots in dynamic contexts - TEL - Thèses en ligneURL: https://theses.hal.science/tel-05559751v1 Description: This thesis is set in a period of unprecedented growth in humanoid robotics, driven both by rapid technological advances and by a growing enthusiasm from private actors and the general public. In such a context, the recent progress in humanoid robot locomotion, together with their increasing social acceptance, seems to announce their forthcoming integration into real environments alongside humans. Faced with this ambition, a major challenge remains: ensuring the robustness and autonomy of locomotion in the dynamic contexts in which these robots will have to operate. To address this challenge, this work combines physical modeling and reinforcement learning, leveraging the complementary advantages of these two paradigms – the stability guarantees provided by modeling and the adaptability arising from learning. This manuscript begins with a state-of-the-art review of control and learning methods applied to humanoid locomotion, aimed at identifying the most promising approaches to reconcile robustness, adaptability, and dynamic realism. On these foundations, the PlaCo software, dedicated to motion planning and robot control, is developed. It aims to abstract the complexity of the optimization formulations required for trajectory generation, while maintaining performance compatible with real-time execution. This framework is then used to design and deploy on the humanoid robot Sigmaban a walking controller based on the Linear Inverted Pendulum Model (LIPM). This development highlights the ability of this model to produce coherent trajectories in real time, while revealing the practical limitations encountered on a real platform. To overcome these limitations and enable dynamic adaptation to disturbances, a reinforcement learning agent dedicated to fall recovery is developed. Trained in simulation, this agent is successfully transferred to the real robot, demonstrating a significant gain in autonomy. However, the difficulty of this transfer highlights the central issue of the gap between simulated and real environments. This observation leads to an investigation of how this gap can be reduced by improving simulation fidelity. A detailed study of friction phenomena in servo actuators is therefore carried out, showing how a more accurate consideration of these effects improves both the quality of simulation and the transferability of control policies. Content:
This thesis is set in a period of unprecedented growth in humanoid robotics, driven both by rapid technological advances and by a growing enthusiasm from private actors and the general public. In such a context, the recent progress in humanoid robot locomotion, together with their increasing social acceptance, seems to announce their forthcoming integration into real environments alongside humans. Faced with this ambition, a major challenge remains: ensuring the robustness and autonomy of locomotion in the dynamic contexts in which these robots will have to operate. To address this challenge, this work combines physical modeling and reinforcement learning, leveraging the complementary advantages of these two paradigms – the stability guarantees provided by modeling and the adaptability arising from learning. This manuscript begins with a state-of-the-art review of control and learning methods applied to humanoid locomotion, aimed at identifying the most promising approaches to reconcile robustness, adaptability, and dynamic realism. On these foundations, the PlaCo software, dedicated to motion planning and robot control, is developed. It aims to abstract the complexity of the optimization formulations required for trajectory generation, while maintaining performance compatible with real-time execution. This framework is then used to design and deploy on the humanoid robot Sigmaban a walking controller based on the Linear Inverted Pendulum Model (LIPM). This development highlights the ability of this model to produce coherent trajectories in real time, while revealing the practical limitations encountered on a real platform. To overcome these limitations and enable dynamic adaptation to disturbances, a reinforcement learning agent dedicated to fall recovery is developed. Trained in simulation, this agent is successfully transferred to the real robot, demonstrating a significant gain in autonomy. However, the difficulty of this transfer highlights the central issue of the gap between simulated and real environments. This observation leads to an investigation of how this gap can be reduced by improving simulation fidelity. A detailed study of friction phenomena in servo actuators is therefore carried out, showing how a more accurate consideration of these effects improves both the quality of simulation and the transferability of control policies. Cette thèse s’inscrit dans une période d’essor sans précédent de la robotique humanoïde, portée à la fois par des avancées technologiques rapides et par un engouement croissant des acteurs privés et du grand public. Dans un tel contexte, les progrès récents en matière de motricité des robots humanoïdes ainsi que leur acceptation sociale grandissante semblent annoncer leur prochaine intégration dans des environnements réels aux côtés des humains. Face à cette ambition, un défi majeur demeure : assurer la robustesse et l’autonomie de la locomotion dans les contextes dynamiques où ces robots seront amenés à évoluer. Pour y répondre, ce travail combine modélisation physique et apprentissage par renforcement, en exploitant les avantages complémentaires de ces deux paradigmes – les garanties de stabilité offertes par la modélisation et la capacité d’adaptation issue de l’apprentissage. Ce manuscrit débute par un état de l’art des méthodes de contrôle et d’apprentissage appliquées à la locomotion humanoïde, destiné à identifier les approches les plus prometteuses pour concilier robustesse, adaptabilité et réalisme dynamique. Sur ces fondations, le logiciel PlaCo, dédié à la planification de mouvement et au contrôle de robots, est développé. Il vise à abstraire la complexité des formulations d’optimisation nécessaires à la génération de trajectoires, tout en maintenant des performances compatibles avec une exécution en temps réel. Ce cadre est ensuite exploité pour concevoir et déployer sur le robot humanoïde Sigmaban un contrôleur de marche fondé sur le modèle du pendule inversé linéaire (LIPM). Ce développement met en évidence la capacité de ce modèle à produire des trajectoires cohérentes en temps réel, tout en révélant les limites pratiques rencontrées sur plateforme réelle. Afin de surmonter ces limitations et de permettre une adaptation dynamique aux perturbations, un agent d’apprentissage par renforcement dédié à la récupération en cas de chute est développé. Entraîné en simulation, cet agent est transféré avec succès sur le robot réel, démontrant un gain significatif en autonomie. La difficulté de ce transfert met néanmoins en évidence la problématique centrale de l’écart entre environnements simulé et réel. Cette observation conduit à chercher comment minimiser cet écart en améliorant la fidélité de la simulation. Une étude approfondie des phénomènes de friction dans les servo-actionneurs est menée, montrant comment une prise en compte plus fine de ces phénomènes améliore la qualité de la simulation et des transferts de politiques. Contact https://theses.hal.science/tel-05559751 Soumis le : jeudi 19 mars 2026-16:31:07 Dernière modification le : lundi 23 mars 2026-10:33:29 Contact Ressources Informations Questions juridiques Portails CCSD
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| Tesla investor shares video of Nvidia-backed Figureâs Humanoid robot cleaning … | https://timesofindia.indiatimes.com/tec… | 1 | Mar 22, 2026 16:00 | active | |
Tesla investor shares video of Nvidia-backed Figureâs Humanoid robot cleaning living room; gets a query from Elon Musk - The Times of IndiaDescription: Tech News News: Tesla investor and influencer, Sawyer Merritt recently shared a video on social media platform X (formerly known as Twitter) showing Nvidia-backed sta. Content:
The TOI Tech Desk is a dedicated team of journalists committed to delivering the latest and most relevant news from the world of technology to readers of The Times of India. TOI Tech Deskâs news coverage spans a wide spectrum across gadget launches, gadget reviews, trends, in-depth analysis, exclusive reports and breaking stories that impact technology and the digital universe. Be it how-tos or the latest happenings in AI, cybersecurity, personal gadgets, platforms like WhatsApp, Instagram, Facebook and more; TOI Tech Desk brings the news with accuracy and authenticity.Read More â¹6,869 â¹10,999
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| Xiaomi usa robot umanoidi per assemblare auto elettriche | https://www.punto-informatico.it/xiaomi… | 1 | Mar 22, 2026 16:00 | active | |
Xiaomi usa robot umanoidi per assemblare auto elettricheURL: https://www.punto-informatico.it/xiaomi-usa-robot-umanoide-assemblare-auto-elettriche/ Description: Due robot umanoidi Xiaomi lavorano sulla linea di produzione delle auto elettriche a Pechino, avvitando dadi ogni 76 secondi. Content:
Due figure bipedi, una per lato della catena di montaggio, avvitano i dadi sulle ruote di un telaio che scorre davanti a loro. Non sono operai. Sono robot umanoidi di Xiaomi, e stanno lavorando nella fabbrica di auto elettriche dell’azienda a Pechino. Non velocissimi, non perfetti, ma funzionanti, e abbastanza rapidi da stare al passo con il resto della linea di produzione. Per un “tirocinante,” come li definisce Xiaomi stessa, non è male. In un’intervista con CNBC, il presidente di Xiaomi Lu Weibing ha spiegato che i due robot hanno completato con successo il 90,2% del lavoro durante un turno di prova di tre ore. Il tempo ciclo è di 76 secondi, lo stesso intervallo con cui un’auto nuova esce dalla linea di produzione della fabbrica. La sfida più grande per integrare i robot nelle nostre linee di produzione è fargli tenere il passo, ha detto Lu. E due robot umanoidi riescono a tenere il nostro ritmo. Un paio di operai umani farebbero lo stesso lavoro più velocemente, senza dubbio. Ma il punto non è la velocità assoluta, è il fatto che dei robot stiano lavorando in un ambiente industriale reale, su un prodotto reale, senza rallentare la produzione. Lu tiene i piedi per terra, per ora sono nella fase sperimentale, ma il risultato è comunque notevole. Un video promozionale dell’azienda mostra i due robot che applicano i dadi con precisione su parti piccole del telaio, un compito che richiede accuratezza millimetrica e che fino a poco tempo fa era considerato fuori dalla portata dei robot umanoidi. Xiaomi non è la prima azienda a mettere robot bipedi in una fabbrica. A febbraio, la britannica Humanoid aveva completato un test simile con un tasso di successo superiore al 90% in un compito di impilamento. La differenza è nei dettagli, i robot Humanoid erano fissati su una base stabile, mentre quelli di Xiaomi sono effettivamente in piedi sulle proprie gambe, come esseri umani. La Cina ha già dispiegato più robot industriali di qualsiasi altro Paese nella storia. Ma i robot industriali tradizionali sono bracci meccanici fissi, progettati per svolgere un compito preciso su una linea di produzione. I robot umanoidi sono un’altra cosa: devono camminare, mantenere l’equilibrio e manipolare oggetti come farebbe una persona. Il passaggio dai bracci robotici ai robot bipedi è quindi un salto tecnologico enorme, anche se per ora si tratta più di una dimostrazione, siamo ancora lontani da una diffusione su larga scala. Tiziana Foglio Pubblicato il 10 mar 2026
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| Toyota залучить гуманоїдних роботів Digit до виробництва автомобілів - ProstoMob | https://prostomob.com/264092-toyota-zal… | 1 | Mar 22, 2026 08:00 | active | |
Toyota залучить гуманоїдних роботів Digit до виробництва автомобілів - ProstoMobURL: https://prostomob.com/264092-toyota-zaluchyt-gumanoyidnyh-robotiv-digit-do-vyrobnycztva-avtomobiliv Description: Заводи з виробництва автомобілів продовжують автоматизувати свої робочі процеси. Компанія Toyota Motor Manufacturing Canada готується до впровадження Content:
Головна » Новини Заводи з виробництва автомобілів продовжують автоматизувати свої робочі процеси. Компанія Toyota Motor Manufacturing Canada готується до впровадження гуманоїдного робота Digit, створеного компанією Agility Robotics, на своєму підприємстві у Вудстоку (Онтаріо), де збирають кросовери Toyota RAV4. Про це повідомляє SlashGear. Хоча промислові роботизовані системи, такі як маніпулятори для зварювання чи фарбування, використовуються в автомобілебудуванні ще з 1960-х років, залучення двоногих гуманоїдів є новим етапом модернізації. На початковому етапі Toyota розгорне трьох роботів із семи, передбачених угодою. Їх задіють у логістиці, виробництві та управлінні ланцюгами постачання. Основна мета — передати машинам рутинні та фізично важкі процеси, що дозволить знизити рівень травматизму серед персоналу та звільнити людей для виконання більш відповідальних завдань. Digit має двоногу конструкцію із шарнірами зворотного типу. Його зріст становить близько 175 сантиметрів, а в головній частині розміщені світлодіодні індикатори для базової комунікації. Завдяки набору датчиків Digit здатний самостійно орієнтуватися в просторі, розпізнавати перешкоди та людей без необхідності встановлення додаткової інфраструктури, такої як магнітні стрічки на підлозі. Крім пересування рівними поверхнями, робот може підійматися пандусами та сходами, а в разі падіння здатен самостійно встати на ноги. Використання гуманоїдних систем стає все більш поширеним серед великих корпорацій — раніше подібних роботів уже впровадили Amazon та GXO Logistics. Наразі Toyota та Agility Robotics планують і надалі досліджувати можливості використання штучного інтелекту та передової робототехніки на автомобільних заводах. Читайте також:
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| Humanoid robot maker Agility rebrands as factories face labor gaps | https://interestingengineering.com/ai-r… | 1 | Mar 22, 2026 08:00 | active | |
Humanoid robot maker Agility rebrands as factories face labor gapsURL: https://interestingengineering.com/ai-robotics/agility-humanoid-robot-maker-rebrands Description: Humanoid maker Agility rebrands as labor shortages push factories toward automation and companies explore robots for industrial work. Content:
From daily news and career tips to monthly insights on AI, sustainability, Aerospace, and more—pick what matters and get it in your inbox. Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies. We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. Follow Us On Access expert insights, exclusive content, and a deeper dive into engineering and innovation. Engineering-inspired textiles, mugs, hats, and thoughtful gifts We connect top engineering talent with the world's most innovative companies We empower professionals with advanced engineering and tech education to grow careers. We recognize outstanding achievements in engineering, innovation, and technology. All Rights Reserved, IE Media, Inc. The robotics firm is planning to focus on broader initiatives while abiding by its promise of delivering humanoid robots. Oregon-based robotics firm Agility has announced it will drop the word ‘Robotics’ from its brand name and adopt a new brand identity to serve a larger cause. The company announced the news via a video and an official blog post on its social media handles. With the shift in identity, Agility plans to explore new use cases, services, and industries to serve, while simultaneously developing humanoid robots. The robotics firm is on track to deliver its first cooperatively safe humanoid robot in 2026. Alongside the name change announced last week, Agility has introduced a new logo and updated brand language inspired by the hardware and software it develops. The company said the redesigned logo is meant to reflect motion, innovation, progress, reliability, and durability. Agility’s humanoid robot, Digit, has already entered the market, demonstrating its readiness to tackle the labor shortage in factories and warehouses. The 5 ft 9 in tall robot set a record milestone in November last year, moving 100,000 totes at a GXO logistics facility. By the end of 2025, Agility partnered with the fintech firm Mercado to deploy Digit to handle physically demanding tasks in its fulfillment network. Earlier in February, automaker Toyota joined a long list of Fortune 500 companies, including Amazon and Schaeffler, in deploying the Digit humanoid robot in warehouses to handle repetitive tasks. A bird’s-eye view of Agility’s current progress and partnerships shows how the firm plans to expand its horizons by working across different industries to address the labor gap. “With our rebrand to Agility, we’re signaling our readiness to scale beyond our current deployments and our ability to lead the adoption of humanoids across many new industries,” said Daniel Diez, Chief Business Officer at Agility. “As we expand into new partnerships and enable new use cases, it is critical that our brand matches the maturity of our technology and our commercial momentum. Agility represents flexibility, durability, and forward motion – qualities our customers need as they integrate humanoids into real operations,” he added. Diez revealed that manufacturers worldwide are struggling to find laborers for highly repetitive physical tasks. “It’s the same exact issue: Labor gaps in these highly repetitive physical tasks. They simply can’t find the people to do this work,” he said in a conversation with Business Insider. As of December 2025, the Bureau of Labor Statistics has reported over 400,000 job openings. According to a 2024 survey conducted across 200 companies, talent retention remains another major concern for manufacturers, contributing to labor scarcity. A significant portion of the manufacturing workforce is 55 and over, approaching retirement, with the BLS Current Population Survey estimating that figure at just over 25%. Added to that, the Trump Administration’s attempts to bring onshore manufacturing back will further increase the need for labor, Diez revealed. “This re-shoring of manufacturing in the US is going to only occur through a combination of human employment and automation technology, like humans and robotics,” he said. With labor pressures mounting and more manufacturers exploring automation, Agility’s long-term trajectory will depend on how successfully it scales Digit across real-world industrial environments. Atharva is a full-time content writer with a post-graduate degree in media & amp; entertainment and a graduate degree in electronics & telecommunications. He has written in the sports and technology domains respectively. In his leisure time, Atharva loves learning about digital marketing and watching soccer matches. His main goal behind joining Interesting Engineering is to learn more about how the recent technological advancements are helping human beings on both societal and individual levels in their daily lives. Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits. Exclusive content, expert insights and a deeper dive into engineering and tech. No ads, no limits. Premium Follow
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| China vs US in Robotics: Nvidia CEO Reveals the Real … | https://www.gizmochina.com/2026/03/21/c… | 1 | Mar 21, 2026 16:00 | active | |
China vs US in Robotics: Nvidia CEO Reveals the Real Power Shift - GizmochinaURL: https://www.gizmochina.com/2026/03/21/china-robotics-dominance-nvidia-physical-ai/ Description: China leads robotics hardware while Nvidia bets on Physical AI to power future machines. Hereâs what it means for the global tech race. Content:
Jensen Huang highlighted that China currently holds a significant edge in robotics, primarily due to its dominance in core components like microelectronics, motors, rare earth materials, and magnets. These elements are foundational to building robots at scale, and Chinaâs ecosystem is described as the âworldâs best.â This deep supply chain strength means even the US robotics industry remains heavily dependent on China for hardware, reinforcing its global influence in the sector. Nvidia is shifting focus beyond traditional GPUs toward what Huang calls Physical AI, the integration of AI into real-world machines like robots and autonomous systems. This marks a major evolution from generative AI to embodied intelligence. Nvidiaâs strategy includes building a full-stack ecosystem, from AI models to robotics infrastructure, positioning itself not just as a chipmaker but as the backbone of future intelligent machines. At its GTC event, Nvidia introduced the Physical AI Data Factory to automate data generation, simulation, and model evaluation. Huang also described a âthree-computerâ model powering robotics: training systems for AI models, simulation platforms like Omniverse, and edge computers embedded in robots. This approach shows robotics is no longer just hardware; it requires a tightly integrated AI ecosystem. While Chinaâs robotics companies, such as Unitree, are scaling rapidly with strong financial growth, Nvidia is navigating a complex market. Its China market share has dropped sharply due to restrictions, but the company is preparing a return with approved H200 AI chips. Despite revenue declines, demand from Chinese firms remains strong. Huang believes robotics adoption is just 3â5 years away from widespread use, driven by exponential growth in AI compute and agentic AI systems. The long-term vision points to a massive economic opportunity, where robots augment human labor across industries. The global balance is becoming clear: China dominates the physical layer, while Nvidia aims to control the intelligence powering it. Read More; (via)
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| Robotic Rabbits Help Researchers Catch Invasive Pythons in the Florida … | https://www.discovermagazine.com/roboti… | 0 | Mar 20, 2026 00:01 | active | |
Robotic Rabbits Help Researchers Catch Invasive Pythons in the Florida EvergladesDescription: Learn about the Burmese python problem in South Florida, and about an innovative initiative that relies on robotic rabbits that look and smell like the real thi... Content: |
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| Arduino Ventuno Q: la nueva placa que integra la IA … | https://hipertextual.com/tecnologia/ard… | 1 | Mar 20, 2026 00:01 | active | |
Arduino Ventuno Q: la nueva placa que integra la IA en robotsURL: https://hipertextual.com/tecnologia/arduino-ventuno-q-placa-robots-ia-qualcomm/ Description: La nueva Ventuno Q de Arduino permite crear robots que ejecutan modelos de IA en el hardware gracias al chip de Qualcomm. Content:
Hipertextual Tecnología, ciencia y cultura digital Arduino ha presentado una nueva placa que permite controlar robots con ayuda de la IA. La compañía aprovechó la víspera del Embedded World para anunciar la Ventuno Q, un nuevo dispositivo capaz de ejecutar tareas de inteligencia artificial sin necesidad de depender de la nube. La nueva placa sigue los pasos de la UNO Q e integra una arquitectura híbrida con chip de Qualcomm y unas especificaciones de software robustas. De acuerdo con el comunicado de prensa, la Ventuno Q monta un procesador Dragonwing IQ8 de Qualcomm. Este chip está diseñado para ejecutar modelos de lenguaje y visión de forma nativa, por lo que los robots no tendrán que conectarse a internet para interpretar las instrucciones. Esta capacidad le permite comprender lenguaje hablado, escrito, reconocer objetos o mapear el entorno por el que se desplazarán usando su propio hardware. Arquitectónicamente, la Ventuno Q utiliza un sistema de doble cerebro como el que encontramos en la UNO Q. Por un lado, la unidad de procesamiento principal se apoya en una NPU que ofrece hasta 40 TOPS para tareas de IA. Por otro lado, el microcontrolador STM32H5 se encarga del control crítico para evitar que el procesamiento de algoritmos interfiera con funciones como la seguridad o el movimiento de un robot. Más allá del enfoque, la diferencia principal con la placa de Arduino que se lanzó a finales de 2025 está en las características de hardware. La Ventuno Q llega con 16 GB de RAM y 64 GB de almacenamiento expandible, un salto notable si consideramos que la UNO Q se vende con 2 GB de RAM y hasta 32 GB de almacenamiento. Este ajuste es necesario debido a los requerimientos de un robot y los modelos de IA. Lo que separa a la Ventuno Q de un ordenador de placa única como el que usamos en el prototipado es el diseño orientado a la acción física. Mientras que una Raspberry Pi suele ser de propósito general, la nueva Arduino integra interfaces industriales nativas como CAN-FD, PWM y GPIO. Estas conexiones permiten que la placa pueda interactuar con máquinas, motores y sistemas de control en entornos robóticos. CAN-FD es un bus de comunicación que permite que sensores, actuadores o controladores se comuniquen rápido y sin contratiempos. PWM es una técnica que regula la velocidad de un motor o la intensidad de una señal de manera precisa. Por último, GPIO son pines de entrada o salida que ayudan a que la Ventuno Q pueda leer sensores o activar actuadores con tiempos de respuesta muy cortos. En la parte de software, Arduino ha optado por una estructura híbrida. El procesador principal corre distribuciones de Linux, mientras que el microcontrolador de tiempo real utiliza el núcleo de Arduino sobre Zephyr OS. Con esta combinación, los desarrolladores pueden optar por Python o C++ según la necesidad, y apoyarse del Qualcomm AI Hub para integrar modelos de reconocimiento de gestos, estimación de postura y más. Por último, el Ventuno Q incluye Ethernet de 2,5 GB y conectores de alta velocidad para cámaras MIPI-CSI. La placa funciona con los shields de la familia UNO, sensores Qwiic y mantiene soporte para HATs de Raspberry Pi, lo que ayudará a los usuarios que ya tienen alguno de estos componentes. Arduino confirmó que la VENTUNO Q estará disponible en el segundo trimestre de 2026 a través de la tienda de Arduino, DigiKey o Mouser. Aunque todavía no existe una web con la configuración, se espera que el precio ronde por debajo de los 300 dólares. Link Copy link
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| Microsoft's New "Physical AI" Could Make Robots Smarter Than Ever | https://propakistani.pk/2026/01/26/micr… | 1 | Mar 19, 2026 08:00 | active | |
Microsoft's New "Physical AI" Could Make Robots Smarter Than EverURL: https://propakistani.pk/2026/01/26/microsofts-new-physical-ai-could-make-robots-smarter-than-ever/ Description: Microsoft has announced Rho-alpha, a new robotics AI model derived from its Phi vision-language series, aimed at helping robots operate more effectively Content:
Microsoft has announced Rho-alpha, a new robotics AI model derived from its Phi vision-language series, aimed at helping robots operate more effectively outside tightly controlled industrial environments. While robots have long performed reliably on assembly lines with predictable conditions, Microsoft argues they often struggle in less structured, real-world settings. The company believes robots need better ways to see, understand instructions, and adapt to changing conditions rather than relying on rigid scripts. Rho-alpha is Microsoft’s first robotics model built on its Phi vision-language framework and is positioned as a step toward what the company describes as “physical AI.” Microsoft links Rho-alpha to the broader shift toward physical AI, where software models guide machines through environments that are not predefined or highly structured. The system combines language, perception, and action in a single model, reducing reliance on fixed production lines and static instructions. Rho-alpha translates natural language commands into robotic control signals, allowing robots to respond dynamically to tasks. A key focus of the model is bimanual manipulation, which requires precise coordination between two robotic arms and fine-grained motor control. Microsoft says Rho-alpha extends traditional vision-language-action approaches by expanding both perception inputs and learning sources. Rho-alpha incorporates tactile sensing alongside visual input, with additional sensing modalities such as force currently under development. These capabilities are designed to help robots better understand physical interactions, narrowing the gap between simulated intelligence and real-world manipulation. Microsoft Research says these design choices aim to improve how robots handle complex tasks in environments where conditions vary and cannot be fully anticipated in advance. Ashley Llorens, Corporate Vice President and Managing Director at Microsoft Research Accelerator, said vision-language-action models are enabling physical systems to perceive, reason, and act with increasing autonomy in environments that are far less structured. A central part of Microsoft’s approach addresses the limited availability of large-scale robotics data, particularly data involving touch. To overcome this, the company relies heavily on simulation. Synthetic trajectories are generated through reinforcement learning using NVIDIA Isaac Sim, and are combined with physical demonstrations sourced from commercial and open datasets. Deepu Talla, Vice President of Robotics and Edge AI at Nvidia, said training foundation models capable of reasoning and acting requires overcoming the scarcity of diverse real-world data. He added that using NVIDIA Isaac Sim on Azure allows Microsoft Research to accelerate the development of models like Rho-alpha that can handle complex manipulation tasks. Microsoft also emphasizes the role of human corrective input during deployment. Operators can intervene using teleoperation devices and provide feedback, which the system can learn from over time. This creates a training loop that blends simulation data, real-world demonstrations, and human correction. The approach reflects a broader trend in robotics toward using AI tools to compensate for limited embodied datasets. Professor Abhishek Gupta, Assistant Professor at the University of Washington, noted that while teleoperated data collection is common, there are many environments where teleoperation is impractical or impossible. He said researchers are working with Microsoft Research to enrich pre-training datasets using diverse synthetic demonstrations generated through simulation and reinforcement learning. 📢 For the latest Tech & Telecom news, videos and analysis join ProPakistani's WhatsApp Group now! Follow ProPakistani on Google News & scroll through your favourite content faster! Shares We are almost there, TERMINATOR a journey from fiction to reality. I’ll be back. ProPakistani is the premier and most trustworthy resource for all happenings in technology, telecom, business, sports, auto, education, real estate and entertainment news in Pakistan. Whether it's the top trending news, inside scoops or features, interviews, market trends and analysis, product reviews, How to's or tutorials – we cover it all. © 2026 ProPakistani.PK - All rights reserved Join the groups below to get latest news and updates. Session expired Please log in again. The login page will open in a new tab. After logging in you can close it and return to this page.
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| Humanoid robot locomotion, footstep planning and fall resilience via reinforcement … | https://theses.hal.science/tel-05549379… | 1 | Mar 19, 2026 08:00 | active | |
Humanoid robot locomotion, footstep planning and fall resilience via reinforcement learning policies - TEL - Thèses en ligneURL: https://theses.hal.science/tel-05549379v1 Description: Humanoid Robot Locomotion, Footstep Planning, and Fall Resilience via Reinforcement Learning Policies explores how to endow humanoid robots with robust locomotion and rapid fall recovery without relying on brittle heuristics or hand-crafted keyframes. The work addresses a central challenge in robotic autonomy: real humanoids must operate reliably in uncertain, contact-rich environments on limited onboard compute. Traditional model-based pipelines often lack adaptability, while deep reinforcement learning (DRL) offers the promise of data-driven, generalizable behaviors. The thesis thus investigates how to design DRL policies that are both computationally efficient and transferable zero-shot from simulation to physical robots, while integrating seamlessly into conventional locomotion stacks. Methodologically, the thesis develops foundational RL algorithms and robotics-oriented architectures, leading to two primary contributions trained in simulation with domain randomization and deployed on small humanoid robots. FootstepNet is an efficient actor-critic footstep planner that generates continuous, task-oriented step placements and, via its critic, predicts the number of steps required to reach multiple local goals—enabling rapid, upstream decision-making. It eliminates reliance on discrete footstep sets and fragile heuristics, supports onboard inference, and matches or surpasses ARA* baseline performance with substantially lower computational cost. FootstepNet was validated both in simulation and on hardware during RoboCup 2023 and 2025. FRASA (Fall Recovery and Stand-up Agent) is a unified, end-to-end policy for fall recovery that directly maps proprioceptive inputs to motor commands, first establishing stabilizing ground contacts before transitioning to a standing pose. Leveraging the Cross-Q algorithm and exploiting robot symmetry, FRASA reduces training time to roughly 30 minutes and transfers zero-shot to real robots, outperforming a keyframe baseline while handling a wide variety of initial postures. Overall, the thesis demonstrates that lightweight, modular DRL policies can achieve practical, safe control for embedded humanoid systems, substantially reducing downtime after disturbances and paving the way for general, learning-based whole-body autonomy in real-world settings. Content:
Humanoid Robot Locomotion, Footstep Planning, and Fall Resilience via Reinforcement Learning Policies explores how to endow humanoid robots with robust locomotion and rapid fall recovery without relying on brittle heuristics or hand-crafted keyframes. The work addresses a central challenge in robotic autonomy: real humanoids must operate reliably in uncertain, contact-rich environments on limited onboard compute. Traditional model-based pipelines often lack adaptability, while deep reinforcement learning (DRL) offers the promise of data-driven, generalizable behaviors. The thesis thus investigates how to design DRL policies that are both computationally efficient and transferable zero-shot from simulation to physical robots, while integrating seamlessly into conventional locomotion stacks. Methodologically, the thesis develops foundational RL algorithms and robotics-oriented architectures, leading to two primary contributions trained in simulation with domain randomization and deployed on small humanoid robots. FootstepNet is an efficient actor-critic footstep planner that generates continuous, task-oriented step placements and, via its critic, predicts the number of steps required to reach multiple local goals—enabling rapid, upstream decision-making. It eliminates reliance on discrete footstep sets and fragile heuristics, supports onboard inference, and matches or surpasses ARA* baseline performance with substantially lower computational cost. FootstepNet was validated both in simulation and on hardware during RoboCup 2023 and 2025. FRASA (Fall Recovery and Stand-up Agent) is a unified, end-to-end policy for fall recovery that directly maps proprioceptive inputs to motor commands, first establishing stabilizing ground contacts before transitioning to a standing pose. Leveraging the Cross-Q algorithm and exploiting robot symmetry, FRASA reduces training time to roughly 30 minutes and transfers zero-shot to real robots, outperforming a keyframe baseline while handling a wide variety of initial postures. Overall, the thesis demonstrates that lightweight, modular DRL policies can achieve practical, safe control for embedded humanoid systems, substantially reducing downtime after disturbances and paving the way for general, learning-based whole-body autonomy in real-world settings. La thèse « Locomotion, planification de pas et résistance aux chutes des robots humanoïdes via des politiques d’apprentissage par renforcement » étudie comment doter les robots humanoïdes d’une marche fiable et d’une capacité de relevage rapide après chute, sans recourir à des heuristiques ni à des trajectoires préprogrammées. Elle s’inscrit dans un enjeu central de l’autonomie robotique : permettre à des robots réels d’agir de façon robuste dans des environnements incertains et riches en contacts, tout en respectant les contraintes du calcul embarqué. Les approches fondées sur des modèles atteignent leurs limites, tandis que l’apprentissage par renforcement profond (DRL) promet des comportements généralisables issus des données. La problématique posée est : comment concevoir des politiques DRL à la fois légères, transférables de la simulation au robot réel et intégrables dans les piles de locomotion existantes ? Méthodologiquement, la thèse présente les fondements de l’apprentissage par renforcement appliqué à la robotique, puis propose deux contributions majeures, entraînées en simulation avec randomisation de domaines et validées sur des humanoïdes de petite taille. FootstepNet est un planificateur de pas acteur-critique efficace, produisant des placements de pas continus tout en anticipant le nombre de pas nécessaires pour atteindre divers objectifs locaux. Il supprime la dépendance aux ensembles discrets et aux heuristiques, fonctionne en inférence embarquée et égale ou dépasse la qualité des planifications d’ARA*, pour un coût de calcul bien moindre, validé en simulation et sur robot réel lors des RoboCup 2023 et 2025. FRASA est un agent unifié de rattrapage et de relevage : une seule politique transforme des observations proprioceptives en commandes moteurs établissant des contacts stabilisateurs avant de se relever. Exploitant l’algorithme Cross-Q et la symétrie du robot, il réduit l’entraînement à environ 30 minutes et se transfère zero-shot sur le robot réel, surpassant une référence à trajectoires préprogrammées et gérant une large variété de postures initiales. En conclusion, ces travaux montrent que des politiques DRL légères, modulaires et sûres peuvent être rendues pratiques pour le contrôle embarqué des humanoïdes, réduisant fortement les temps d’indisponibilité après perturbation et ouvrant la voie à une autonomie plus robuste en conditions réelles. Contact https://theses.hal.science/tel-05549379 Soumis le : jeudi 12 mars 2026-15:03:08 Dernière modification le : vendredi 13 mars 2026-03:19:37 Contact Ressources Informations Questions juridiques Portails CCSD
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| NXP unveils robotics solutions with NVIDIA Holoscan for real-time AI | https://www.fonearena.com/blog/477663/n… | 1 | Mar 19, 2026 00:04 | active | |
NXP unveils robotics solutions with NVIDIA Holoscan for real-time AIURL: https://www.fonearena.com/blog/477663/nxp-robotics-solutions-nvidia-holoscan.html Content:
Fone Arena The Mobile Blog NXP Semiconductors has announced a new set of robotics solutions focused on reliable, secure, real-time data processing, transport, and advanced networking. Developed in collaboration with NVIDIA, these ready-to-deploy solutions mark the first in a series of NXP’s foundational robotics platforms. The solutions integrate NVIDIA Holoscan Sensor Bridge with NXP’s highly integrated system-on-chips (SoCs). This reduces discrete components, lowers power consumption and system cost, and simplifies software complexity for robotic sensing and actuation, including humanoid form factors. NXP positions physical AI as a category of systems capable of sensing, interpreting, and interacting with real-world environments with precision, reliability, and safety. Humanoid robots are among the most advanced implementations, requiring low-latency data processing and transport across the robot body to support synchronized motion, dense sensor fusion, and advanced actuation. To address these requirements, NXP’s integrated robot body solutions deliver edge intelligence and low-latency networking for real-time communication. The platform embeds NVIDIA Holoscan Sensor Bridge into NXP’s software environment, enabling real-time processing and establishing a direct transport path between the robot body and pre-specified regions of the robot brain, reducing latency in data flow. The joint architecture combines: This creates a flexible and energy-efficient system architecture for full-body humanoid robotics. NXP’s first Holoscan Sensor Bridge-ready solutions include machine vision and precision motor control systems. These are designed to handle high-bandwidth sensor data, enable synchronized motion, and support real-time decision-making across humanoid robot bodies. Key components include: These software-driven, highly integrated solutions provide a complete and scalable foundation for full-body humanoid robot design. NXP stated that these robotics solutions will be available in the first half of 2026 (1H 2026). Speaking on the robotics solutions, Charles Dachs, Executive Vice President and General Manager, Secure Connected Edge, NXP Semiconductors, said: Physical AI is redefining what machines can achieve in the real world, and humanoid robots represent the most complex expression of that revolution. By combining NXP’s deep expertise in edge processing, secure networking, functional safety, and real-time control with NVIDIA robotics platforms, we are greatly simplifying physical AI development, enabling seamless connectivity between the physical AI edge and the central brain. This is just the beginning of what NXP will deliver to accelerate the ecosystem for physical AI. Commenting on the development, Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, said: The development of autonomous machines requires a high-performance computing architecture that can synchronize complex motor controls with real-time perception. By integrating NVIDIA Holoscan Sensor Bridge into its edge portfolio, NXP is offering developers a scalable foundation to accelerate the deployment of physical AI.
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| NVIDIA and Global Robotics Leaders Take Physical AI to the … | https://www.manilatimes.net/2026/03/17/… | 0 | Mar 19, 2026 00:04 | active | |
NVIDIA and Global Robotics Leaders Take Physical AI to the Real WorldDescription: NVIDIA and Global Robotics Leaders Take Physical AI to the Real World Content: |
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| National AI model project race heats up as consortia expand … | https://www.koreatimes.co.kr/business/t… | 1 | Mar 18, 2026 16:00 | active | |
National AI model project race heats up as consortia expand new AI partners - The Korea TimesDescription: The government-led artificial intelligence (AI) foundation model project is intensifying its race as the four consortia in the second-round evaluat... Content:
Attendees visit Upstage's booth during a presentation for the national artificial intelligence foundation model project at Coex in Seoul, Dec. 30, 2025. Yonhap The government-led artificial intelligence (AI) foundation model project is intensifying its race as the four consortia in the second-round evaluation bulk up with new specialized partners spanning 3D AI, large language model (LLM) inference chips and high‑end training data. The Ministry of Science and ICT recently selected a consortium led by Motif Technologies to advance to the project’s second round, filling a vacant slot after only three teams — LG AI Research, SK Telecom and Upstage — moved on from the first round of evaluations in January. Each consortium is now rushing to bring in partners that boost its technical edge ahead of the next evaluation, evolving a model-building contest into creating a globally competitive sovereign AI stack from models to real-world deployment. LG AI Research announced on Feb. 25 that AI infrastructure and solutions provider Elice has joined its consortium to help commercialize its model K-EXAONE across public and private sectors. Elice will leverage its modular data center infrastructure to expose the AI model through stable application programming interfaces (APIs), offering a managed AI platform that lets public agencies and enterprises spin up dedicated environments without operational burden. The company also plans to deploy the AI model in security‑sensitive sectors such as manufacturing, finance and government, offering cloud‑based services for workflow automation, retrieval-augmented generation (RAG)‑powered search and document-generation tools. A visitor tries LG AI Research's artificial intelligence (AI) model K-EXAONE during a presentation event for the national AI foundation model project at Coex in Seoul, Dec. 30, 2025. Yonhap Meanwhile, Upstage is reinforcing its consortium with AI semiconductor startup HyperAccel and physical AI startup RLWRLD. HyperAccel has been developing an LLM processing unit (LPU) based on its own chip architecture to ease inference bottlenecks, and cut power and operating costs for LLM services. As part of the team, the company plans to further refine LPU design and performance, build inference acceleration optimized for generative AI workloads and roll out a high-availability, full-stack software platform. "The race in ultralarge AI models is not just about how big they are, but how efficiently you can serve them," HyperAccel CEO Kim Joo-young said. "With LPU-based inference acceleration, we aim to help Korea’s AI infrastructure stand on its own technologically and reach global-level cost competitiveness." RLWRLD is joining the team to help bridge Upstage’s multimodal AI model, Solar, into real‑world robotics deployment. The company will define vision‑language model (VLM) requirements for robot control optimization and integration with robotics foundation models and identify commercially viable tasks in hotels, logistics and retail. It will also co‑design detailed validation scenarios and test protocols that translate Solar’s capabilities into robots that can see, understand and act in real‑world settings. Motif Technologies announced that 3D AI startup N.Light and AI training data platform Crowdworks additionally joined its consortium, which aims to build a 300-billion-parameter LLM and scale it into VLM and vision-language‑action models (VLAs). N.Light will develop an AI-based 3D data pipeline that turns text or images directly into manufacturable, high-precision 3D computer-aided design models and automatically converts them into formats that simulators can use. It will also generate large synthetic datasets, collected through simulation, to train VLA models that jointly control vision, language and action, which are essential for physical-AI learning. Crowdworks is taking the role of core data provider for the team’s AI model to deliver high-quality data. It will focus its core capabilities on building datasets specialized for step‑by‑step reasoning to maximize the model’s capacity for intelligent reasoning. It will also deploy its proprietary unstructured data preprocessing solution, Alpy Knowledge Compiler, to convert complex documents such as tables and charts into data that AI systems can understand. Robotics company XYZ also joined the consortium to provide real-world data it has collected through its robots, while also gathering and refining multimodal datasets on human-robot interaction and high-precision manipulation data using its proprietary system. SK Telecom has not announced any additional members to its consortium since the first round.
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| Qualcomm's partnership with Neura Robotics is just the beginning | … | https://techcrunch.com/2026/03/09/qualc… | 1 | Mar 16, 2026 08:00 | active | |
Qualcomm's partnership with Neura Robotics is just the beginning | TechCrunchURL: https://techcrunch.com/2026/03/09/qualcomms-partnership-with-neura-robotics-is-just-the-beginning/ Description: Neura Robotics is going to build new robots on top of Qualcomm's new IQ10 processors that were released at CES. Content:
TechCrunch Founder Summit 2026: Last day for ticket savings of up to $300. Register Now. Save up to $680 on your Disrupt 2026 pass. Ends 11:59 p.m. PT tonight. REGISTER NOW. Latest AI Amazon Apps Biotech & Health Climate Cloud Computing Commerce Crypto Enterprise EVs Fintech Fundraising Gadgets Gaming Google Government & Policy Hardware Instagram Layoffs Media & Entertainment Meta Microsoft Privacy Robotics Security Social Space Startups TikTok Transportation Venture Staff Events Startup Battlefield StrictlyVC Newsletters Podcasts Videos Partner Content TechCrunch Brand Studio Crunchboard Contact Us German robotics startup Neura Robotics has inked a partnership with semiconductor giant Qualcomm to build the next generation of robots and physical AI. The deal is the latest coupling in the emerging physical AI industry between robotics startups and larger tech hardware and software companies. While no specific products were mentioned in the Monday announcements, the companies will work together to build the “brain and nervous system” of robots in a quest to advance the deployment of humanoid and general-purpose robots in the real world in both domestic and industrial settings. More specifically, Neura will use Qualcomm’s Dragonwing Robotics IQ10 processors as reference designs in its robots. This IQ10 series was announced at CES earlier this year, and these chips are designed to work with autonomous mobile robots (AMRs) and humanoids. Neura also plans to use its Neuraverse robotic simulation and training platform, which was released in June 2025, to test and fine-tune the robots running on Qualcomm’s IQ10 processors. “This collaboration marks a major step toward making physical AI real: open, scalable, and trusted,” David Reger, CEO and founder of Neura Robotics, said in a press release. “By bringing together our cognitive robotics platforms and the Neuraverse ecosystem with Qualcomm Technologies’ leadership in edge AI and connectivity, we’re aiming to accelerate a future where cognitive robots operate safely alongside humans across industries and throughout everyday life.” This deal makes a lot of sense for both sides. And it’s a formula that will likely become a popular strategy for robotics companies trying to bring their products into the real world. For instance, Boston Dynamics announced a strategic partnership with Google DeepMind in January to speed up the development of the robotic company’s Atlas humanoid robot by using Google’s AI foundational models. While Boston Dynamics and Neura’s respective partnerships deal with different technologies — AI models versus chips — the same conclusion can be drawn. Instead of these two companies just being customers of tech vendors, partnering allows for these robotic companies to better use and embed these technologies. A robotic company that has technical prowess in software will have a much easier — and likely cheaper — path to market and scale through partnering with hardware companies that have already figured out tough technical challenges like building robotics hands with dexterity, for example. In Neura’s case, the company gets to build and test robots designed for the chips they are running on while Qualcomm gets an intimate look at how robotic companies can use its processors. As more AI companies like Nvidia look to physical AI as the next major market for their technology, they are going to want a seat at the table of how their tech is being used. The upshot: expect more partnerships. Topics Senior Reporter, Venture Becca is a senior writer at TechCrunch that covers venture capital trends and startups. She previously covered the same beat for Forbes and the Venture Capital Journal. You can contact or verify outreach from Becca by emailing rebecca.szkutak@techcrunch.com. Actively scaling? Fundraising? Planning your next launch?TechCrunch Founder Summit 2026 delivers tactical playbooks and direct access to 1,000+ founders and investors who are building, backing, and closing.Register by March 13 to save up to $300. ‘Not built right the first time’ — Musk’s xAI is starting over again, again Lovable says it added $100M in revenue last month alone, with just 146 employees DOGE employee stole Social Security data and put it on a thumb drive, report says Meta acquired Moltbook, the AI agent social network that went viral because of fake posts Google rolls out new Gemini capabilities to Docs, Sheets, Slides, and Drive Yann LeCun’s AMI Labs raises $1.03B to build world models Anthropic launches code review tool to check flood of AI-generated code © 2026 TechCrunch Media LLC.
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| Qualcomm Launches Dragonwing Robotics Hub, Announces NEURA AI Robotics Collaboration … | https://hothardware.com/news/qualcomm-l… | 1 | Mar 16, 2026 08:00 | active | |
Qualcomm Launches Dragonwing Robotics Hub, Announces NEURA AI Robotics Collaboration | HotHardwareDescription: Qualcomm and NEURA Robotics have announced a collaboration to integrate edge computing with embodied AI, aiming to move cognitive humanoid robots from research labs into large-scale industrial and domestic environments. Content:
Aaron Leong Stay updated with the latest news and updates. Subscribe to our newsletter! Home Reviews News Blogs Full Site Sitemap PC Components Systems Mobile IT Infrastructure Leisure Videos About Advertise News Tips Contact Privacy Policy HotTech Accessibility Shop Twitter Facebook YouTube RSS Or sign in manually:
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| Should service robots have rights? | https://www.hospitalitynet.org/news/413… | 0 | Mar 16, 2026 00:03 | active | |
Should service robots have rights?URL: https://www.hospitalitynet.org/news/4130338.html Description: From hotel room deliveries to cooking, the use of service robots — some containing human characteristics in terms of appearance and communication — has grow... Content: |
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| This ‘Machine Eye’ Could Give Robots Superhuman Reflexes | https://singularityhub.com/2026/02/19/t… | 1 | Mar 14, 2026 16:00 | active | |
This ‘Machine Eye’ Could Give Robots Superhuman ReflexesURL: https://singularityhub.com/2026/02/19/this-machine-eye-could-give-robots-superhuman-reflexes/ Description: Running on a brain-like chip, the 'eye' could help robots and self-driving cars make split-second decisions. Content:
Running on a brain-like chip, the 'eye' could help robots and self-driving cars make split-second decisions. Image Credit Amanda Dalbjörn on Unsplash Share You’re driving in a winter storm at midnight. Icy rain smashes your windshield, immediately turning it into a sheet of frost. Your eyes dart across the highway, seeking any movement that could be wildlife, struggling vehicles, or highway responders trying to pass. Whether you find safe passage or meet catastrophe hinges on how fast you see and react. You’re driving in a winter storm at midnight. Icy rain smashes your windshield, immediately turning it into a sheet of frost. Your eyes dart across the highway, seeking any movement that could be wildlife, struggling vehicles, or highway responders trying to pass. Whether you find safe passage or meet catastrophe hinges on how fast you see and react. Even experienced drivers struggle with bad weather. For self-driving cars, drones, and other robots, a snowstorm could cause mayhem. The best computer-vision algorithms can handle some scenarios, but even running on advanced computer chips, their reaction times are roughly four times greater than a human’s. Even experienced drivers struggle with bad weather. For self-driving cars, drones, and other robots, a snowstorm could cause mayhem. The best computer-vision algorithms can handle some scenarios, but even running on advanced computer chips, their reaction times are roughly four times greater than a human’s. “Such delays are unacceptable for time-sensitive applications…where a one-second delay at highway speeds can reduce the safety margin by up to 27m [88.6 feet], significantly increasing safety risks,” Shuo Gao at Beihang University and colleagues wrote in a recent paper describing a new superfast computer vision system. “Such delays are unacceptable for time-sensitive applications…where a one-second delay at highway speeds can reduce the safety margin by up to 27m [88.6 feet], significantly increasing safety risks,” Shuo Gao at Beihang University and colleagues wrote in a recent paper describing a new superfast computer vision system. Instead of working on the software, the team turned to hardware. Inspired by the way human eyes process movement, they developed an electronic replica that rapidly detects and isolates motion. Instead of working on the software, the team turned to hardware. Inspired by the way human eyes process movement, they developed an electronic replica that rapidly detects and isolates motion. The machine eye’s artificial synapses connect transistors into networks that detect changes in the brightness of an image. Like biological neural circuits, these connections store a brief memory of the past before processing new inputs. Comparing the two allows them to track motion. The machine eye’s artificial synapses connect transistors into networks that detect changes in the brightness of an image. Like biological neural circuits, these connections store a brief memory of the past before processing new inputs. Comparing the two allows them to track motion. Combined with a popular vision algorithm, the system quickly separates moving objects, like walking pedestrians, from static objects, like buildings. By limiting its attention to motion, the machine eye needs far less time and energy to assess and respond to complex environments. Combined with a popular vision algorithm, the system quickly separates moving objects, like walking pedestrians, from static objects, like buildings. By limiting its attention to motion, the machine eye needs far less time and energy to assess and respond to complex environments. When tested on autonomous vehicles, drones, and robotic arms, the system sped up processing times by roughly 400 percent and, in most cases, surpassed the speed of human perception without sacrificing accuracy. When tested on autonomous vehicles, drones, and robotic arms, the system sped up processing times by roughly 400 percent and, in most cases, surpassed the speed of human perception without sacrificing accuracy. “These advancements empower robots with ultrafast and accurate perceptual capabilities, enabling them to handle complex and dynamic tasks more efficiently than ever before,” wrote the team. “These advancements empower robots with ultrafast and accurate perceptual capabilities, enabling them to handle complex and dynamic tasks more efficiently than ever before,” wrote the team. A mere flicker in the corner of an eye captures our attention. We’ve evolved to be especially sensitive to movement. This perceptual superpower begins in the retina. The thin layer of light-sensitive tissue at the back of the eye is packed with cells fine-tuned to detect motion. A mere flicker in the corner of an eye captures our attention. We’ve evolved to be especially sensitive to movement. This perceptual superpower begins in the retina. The thin layer of light-sensitive tissue at the back of the eye is packed with cells fine-tuned to detect motion. Retinal cells are a curious bunch. They store memories of previous scenes and spark with activity when something in our visual field shifts. The process is a bit like an old-school film reel: Rapid transitions between still frames lead to the perception of movement. Retinal cells are a curious bunch. They store memories of previous scenes and spark with activity when something in our visual field shifts. The process is a bit like an old-school film reel: Rapid transitions between still frames lead to the perception of movement. Every cell is tuned to detect visual changes in a particular direction—for example, left to right or up to down—but is otherwise dormant. These activity patterns form a two-dimensional neural map that the brain interprets as speed and direction within a fraction of a second. Every cell is tuned to detect visual changes in a particular direction—for example, left to right or up to down—but is otherwise dormant. These activity patterns form a two-dimensional neural map that the brain interprets as speed and direction within a fraction of a second. “Biological vision excels at processing large volumes of visual information” by focusing only on motion, wrote the team. When driving across an intersection, our eyes intuitively zero in on pedestrians, cyclists, and other moving objects. “Biological vision excels at processing large volumes of visual information” by focusing only on motion, wrote the team. When driving across an intersection, our eyes intuitively zero in on pedestrians, cyclists, and other moving objects. Computer vision takes a more mathematical approach. Computer vision takes a more mathematical approach. A popular type called optical flow analyzes differences between pixels across visual frames. The algorithm segments pixels into objects and infers movement based on changes in brightness. This approach assumes that objects maintain brightness as they move. A white dot, for example, remains a white dot as it drifts to the right, at least in simulations. Pixels near each other should also move in tandem as a marker for motion. A popular type called optical flow analyzes differences between pixels across visual frames. The algorithm segments pixels into objects and infers movement based on changes in brightness. This approach assumes that objects maintain brightness as they move. A white dot, for example, remains a white dot as it drifts to the right, at least in simulations. Pixels near each other should also move in tandem as a marker for motion. Although inspired by biological vision, optical flow struggles in real-world scenarios. It’s an energy hog and can be laggy. Add in unexpected noise—like a snowstorm—and robots running optical flow algorithms will have trouble adapting to our messy world. Although inspired by biological vision, optical flow struggles in real-world scenarios. It’s an energy hog and can be laggy. Add in unexpected noise—like a snowstorm—and robots running optical flow algorithms will have trouble adapting to our messy world. Sign up to receive top stories about groundbreaking technologies and visionary thinkers from SingularityHub. To get around these problems, Gao and colleagues built a neuron-inspired chip that dynamically detects regions of motion and then focuses an optical flow algorithm on only those areas. To get around these problems, Gao and colleagues built a neuron-inspired chip that dynamically detects regions of motion and then focuses an optical flow algorithm on only those areas. Their initial design immediately hit a roadblock. Traditional computer chips can’t adjust their wiring. So the team fabricated a neuromorphic chip that, true to its name, computes and stores information at the same spot, much like a neuron processes data and retains memory. Their initial design immediately hit a roadblock. Traditional computer chips can’t adjust their wiring. So the team fabricated a neuromorphic chip that, true to its name, computes and stores information at the same spot, much like a neuron processes data and retains memory. Because neuromorphic chips don’t shuttle data from memory to processors, they’re far faster and more energy-efficient than classical chips. They outshine standard chips in a variety of tasks, such as sensing touch, detecting auditory patterns, and processing vision. Because neuromorphic chips don’t shuttle data from memory to processors, they’re far faster and more energy-efficient than classical chips. They outshine standard chips in a variety of tasks, such as sensing touch, detecting auditory patterns, and processing vision. “The on-device adaptation capability of synaptic devices makes human-like ultrafast visual processing possible,” wrote the team. “The on-device adaptation capability of synaptic devices makes human-like ultrafast visual processing possible,” wrote the team. The new chip is built from materials and designs commonly used in other neuromorphic chips. Similar to the retina, the array’s artificial synapses encode differences in brightness and remember these changes by adjusting their responses to subsequent electrical signals. The new chip is built from materials and designs commonly used in other neuromorphic chips. Similar to the retina, the array’s artificial synapses encode differences in brightness and remember these changes by adjusting their responses to subsequent electrical signals. When processing an image, the chip converts the data into voltage changes, which only activate a handful of synaptic transistors; the others stay quiet. This means the chip can filter out irrelevant visual data and focus optical flow algorithms on regions with motion only. When processing an image, the chip converts the data into voltage changes, which only activate a handful of synaptic transistors; the others stay quiet. This means the chip can filter out irrelevant visual data and focus optical flow algorithms on regions with motion only. In tests, the two-step setup boosted processing speed. When analyzing a movie of a pedestrian about to dash across a road, the chip detected their subtle body position and predicted what direction they’d run in roughly 100 microseconds—faster than a human. Compared to conventional computer vision, the machine eye roughly doubled the ability of self-driving cars to detect hazards in a simulation. It also improved the accuracy of robotic arms by over 740 percent thanks to better and faster tracking. In tests, the two-step setup boosted processing speed. When analyzing a movie of a pedestrian about to dash across a road, the chip detected their subtle body position and predicted what direction they’d run in roughly 100 microseconds—faster than a human. Compared to conventional computer vision, the machine eye roughly doubled the ability of self-driving cars to detect hazards in a simulation. It also improved the accuracy of robotic arms by over 740 percent thanks to better and faster tracking. The system is compatible with computer vision algorithms beyond optical flow, such as the YOLO neural network that detects objects in a scene, making it adjustable for different uses. The system is compatible with computer vision algorithms beyond optical flow, such as the YOLO neural network that detects objects in a scene, making it adjustable for different uses. “We do not completely overthrow the existing camera system; instead, by using hardware plug-ins, we enable existing computer vision algorithms to run four times faster than before, which holds greater practical value for engineering applications,” Gao told the South China Morning Post. “We do not completely overthrow the existing camera system; instead, by using hardware plug-ins, we enable existing computer vision algorithms to run four times faster than before, which holds greater practical value for engineering applications,” Gao told the South China Morning Post. Dr. Shelly Xuelai Fan is a neuroscientist-turned-science-writer. She's fascinated with research about the brain, AI, longevity, biotech, and especially their intersection. As a digital nomad, she enjoys exploring new cultures, local foods, and the great outdoors. Related Articles What we’re reading Sign up to receive top stories about groundbreaking technologies and visionary thinkers from SingularityHub. SingularityHub chronicles the technological frontier with coverage of the breakthroughs, players, and issues shaping the future.
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| ABB Robotics anuncia parceria com a NVIDIA em IA física … | https://overbr.com.br/parcerias/abb-rob… | 1 | Mar 14, 2026 08:00 | active | |
ABB Robotics anuncia parceria com a NVIDIA em IA física - OverBRURL: https://overbr.com.br/parcerias/abb-robotics-anuncia-parceria-com-a-nvidia-em-ia-fisica Description: Além disso, a ABB Robotics também avalia integrar a plataforma de computação de borda NVIDIA Jetson ao seu controlador Omnicore... Content:
Integração entre RobotStudio® da ABB Robotics e tecnologias NVIDIA Omniverse permitirá simulações industriais com até 99% de precisão; Tecnologia pode reduzir custos de produção em até 40% e acelerar em até 50% o lançamento de novos produtos A ABB Robotics anuncia hoje uma parceria com a NVIDIA para acelerar a adoção de IA física na indústria, integrando sua plataforma de simulação RobotStudio® às tecnologias NVIDIA Omniverse. Mais detalhes sobre a colaboração serão apresentados em uma coletiva virtual nesta segunda-feira, 9 de março, às 12h (horário de Brasília). Jornalistas poderão acompanhar o evento e acessar imagens e materiais de apoio, que estarão disponíveis após a coletiva. A iniciativa permitirá que fabricantes simulem robôs em gêmeos digitais e utilizem dados sintéticos para treinar modelos de inteligência artificial antes da implantação em ambientes reais, reduzindo a chamada lacuna entre simulação e aplicação prática — conhecida como “sim-to-real gap”. “Hoje, utilizando tecnologias de computação acelerada e simulação da NVIDIA, eliminamos as últimas barreiras para tornar a IA industrial e física uma realidade em escala global”, afirma Marc Segura, presidente da ABB Robotics. “Há mais de 50 anos, a ABB lidera a evolução da automação industrial, e essa colaboração marca um novo passo para levar a IA física à indústria”, completa o executivo. A integração permitirá criar simulações fisicamente precisas e gerar dados sintéticos para treinar robôs em diferentes cenários industriais. Com isso, empresas poderão testar e otimizar processos produtivos virtualmente antes da implementação nas fábricas. A solução resultante, chamada RobotStudio HyperReality, terá suas primeiras versões disponibilizadas para clientes selecionados antes do lançamento global previsto para o segundo semestre de 2026. Segundo a ABB, a tecnologia poderá reduzir em até 80% o tempo de configuração de linhas de produção, diminuir custos em até 40% ao eliminar protótipos físicos e acelerar em até 50% o lançamento de produtos complexos, como eletrônicos de consumo. “Integrar as tecnologias NVIDIA Omniverse ao RobotStudio leva simulação avançada e computação acelerada ao controlador virtual exclusivo da ABB”, afirma Deepu Talla, vice-presidente de robótica e IA de edge da NVIDIA. “Isso acelera a forma como fabricantes de todos os tamanhos levam produtos complexos ao mercado”, explica. Entre os primeiros casos de uso da tecnologia está um projeto piloto com a Foxconn, maior fabricante terceirizada de eletrônicos do mundo. A empresa está utilizando a solução para treinar virtualmente robôs responsáveis pela montagem de componentes em dispositivos eletrônicos, com dados sintéticos para aperfeiçoar processos produtivos antes da implementação na linha de produção. De acordo com Dr. Zhe Shi, Chief Digital Officer da Foxconn, o uso da tecnologia permitirá maior precisão e velocidade na produção de eletrônicos de consumo. “Precisão é essencial na fabricação de eletrônicos e, até agora, esse nível de fidelidade simplesmente não era possível em simulações e gêmeos digitais”, afirma. Outra empresa envolvida na aplicação da tecnologia é a WORKR, companhia de força de trabalho robótica baseada na Califórnia. A empresa utilizará a plataforma para levar automação baseada em IA a pequenos e médios fabricantes nos Estados Unidos. Durante a NVIDIA GTC 2026, que acontece entre 16 e 19 de março em San Jose, na Califórnia, a WORKR demonstrará sistemas robóticos baseados em tecnologia da ABB, treinados com dados sintéticos e capazes de executar novas tarefas em poucos minutos. Segundo Ken Macken, CEO e fundador da WORKR, a colaboração demonstra que a automação avançada pode ser aplicada em empresas de qualquer porte. “Junto com ABB e NVIDIA, estamos mostrando que a IA industrial pode ser implementada hoje e ajudar fabricantes a enfrentar desafios como a escassez de mão de obra”, acrescenta Ken. Além disso, a ABB Robotics também avalia integrar a plataforma de computação de borda NVIDIA Jetson ao seu controlador Omnicore, permitindo inferência de IA em tempo real diretamente nos robôs. A ABB destaca que é a única fabricante de robôs com um controlador virtual que utiliza o mesmo firmware do hardware, garantindo alta fidelidade entre simulação e desempenho no mundo real. Combinada com a tecnologia Absolute Accuracy, que reduz erros de posicionamento para cerca de 0,5 milímetro, a solução busca oferecer alta precisão em aplicações industriais. Coletiva virtual e materiais Para apresentar a parceria e os detalhes do RobotStudio HyperReality, a ABB Robotics realizou uma coletiva virtual na segunda-feira, 9 de março, às 12h (horário de Brasília). A ABB Robótica é líder em seu setor, posicionada no centro das tendências estruturais e futuras de automação. Conforme comunicado anteriormente, há sinergias limitadas de negócios e tecnologia entre a ABB Robótica e as demais divisões do Grupo ABB, que apresentam diferentes características de demanda e de mercado. A divisão ABB Robótica conta com aproximadamente 7.000 colaboradores. Com receitas de US$ 2,3 bilhões em 2024, representou cerca de 7% das receitas do Grupo ABB, com uma margem de EBITA Operacional de 12,1%. A ABB é uma líder global em tecnologia nas áreas de eletrificação e automação, viabilizando um futuro mais sustentável e eficiente no uso de recursos. Ao integrar sua expertise em engenharia e digitalização, a ABB apoia indústrias a operarem com alto desempenho, tornando-se mais eficientes, produtivas e sustentáveis, para que possam superar seus resultados. Na ABB, chamamos isso de “Engineered to Outrun”. A empresa possui mais de 140 anos de história e cerca de 110.000 colaboradores em todo o mundo. As ações da ABB estão listadas na SIX Swiss Exchange (ABBN) e na Nasdaq Stockholm (ABB). Salve meu nome, email e site neste navegador para a próxima vez que eu comentar. Δ Ao navegar neste site, você aceita os cookies que usamos para melhorar sua experiência. Aceito Mais informações OverBR - 2008 | © Copyright 2021 , All Rights Reserved. | Nós Confiamos em Deus. | Privacidade | Anuncie | Otimizador de Site.
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| Qrypt Releases Post-Quantum VPN for NVIDIA Jetson Robotics - Quantum … | https://quantumcomputingreport.com/qryp… | 1 | Mar 14, 2026 08:00 | active | |
Qrypt Releases Post-Quantum VPN for NVIDIA Jetson Robotics - Quantum Computing ReportURL: https://quantumcomputingreport.com/qrypt-releases-post-quantum-vpn-for-nvidia-jetson-robotics/ Description: Qrypt has launched a post-quantum secure VPN solution specifically designed for the NVIDIA Jetson Orin (and upcoming Jetson Thor) platforms to protect robotics data from "harvest now, decrypt later" Content:
Qrypt has launched a post-quantum secure VPN solution specifically designed for the NVIDIA Jetson Orin (and upcoming Jetson Thor) platforms to protect robotics data from “harvest now, decrypt later” (HNDL) attacks. Robotics systems, such as autonomous mobile robots (AMRs) and drones, often remain in the field for over a decade, making their sensor streams, navigation maps, and telemetry vulnerable to future quantum-enabled decryption. The solution utilizes a Hybrid PQC IPsec framework, allowing engineers to implement quantum-resilient key exchanges without sacrificing real-time performance or overhauling existing stacks. The architecture integrates strongSwan 6.0 with liboqs for ML-KEM (Kyber) and the Qrypt BLAST plugin for quantum-secure key generation. To support this on embedded hardware, Qrypt engineered a custom Yocto Project distribution that upgrades the NVIDIA kernel from the stock 5.15 to the 6.6 LTS version, which is required for PQC Child SA rekeying support. This technical stack enables a high-throughput tunnel (benchmarked at 926 Mbps) with less than 1% overhead compared to classical encryption, ensuring that latency-sensitive robotics applications—like remote teleoperation or operator video—maintain high performance while achieving “intelligence-grade” security. Beyond standard PQC algorithms, the integration of Qrypt BLAST provides a second layer of defense by replacing traditional key-distribution architectures. While standard Post-Quantum Cryptography still transmits keys over the network, BLAST allows endpoints to independently generate matching keys from quantum entropy sourced from NIST ESV certified random number generators. This “turn the dial to maximum” security option eliminates the risk of key interception during transit and supports asynchronous key buffering, which helps the VPN maintain stability and low latency even under network jitter or intermittent cellular backhaul. Qrypt provides pre-built images and reproducible build instructions, allowing robotics engineers to deploy a post-quantum secure stack in approximately 40 minutes. Support for the next-generation NVIDIA Jetson Thor AGX is currently in development and targeted for a February 2026 release, which will further integrate with the platform’s improved TrustZone and hardware security modules. This initiative represents a critical step in future-proofing industrial infrastructure and ensuring that sensitive proprietary navigation models and facility data remain protected for their entire operational lifetime. For full technical configuration, kernel options, and the Jetson Yocto build repository, consult the official Qrypt blog post here and the technical BLAST integration documentation here. March 13, 2026 Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.
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| ABB Robotics will mit Nvidia physische KI in der Industrie … | https://www.elektrotechnik.vogel.de/abb… | 1 | Mar 14, 2026 08:00 | active | |
ABB Robotics will mit Nvidia physische KI in der Industrie nutzbar machenDescription: Durch die Integration der Nvidia Omniverse-Bibliotheken in ABBs Robot Studio soll die Skalierung industrieller Produktion stark beschleunigen können. Content:
Anbieter zum Thema Durch die Integration der Nvidia Omniverse-Bibliotheken in ABBs Robot Studio soll die Skalierung industrieller Produktion stark beschleunigen können. ABB Robotics hat die Integration der Nvidia-Omniverse-Bibliotheken in seine Simulations- und Programmierplattform Robot Studio angekündigt und damit den Weg für den breiten industriellen Einsatz physischer KI geebnet. Laut einer Mitteilung soll die Kombination aus ABB-Robotiksoftware und physikalisch präziser Simulation die bislang bestehende „Sim-to-Real“-Lücke zwischen virtueller Entwicklung und realer Produktion nahezu vollständig schließen. Durch die Verknüpfung digitaler Zwillinge mit realitätsnahen Simulationen erreichen Roboter laut Unternehmen eine Übereinstimmung von bis zu 99 Prozent zwischen Modell und Anwendung. Entwickler können Roboterprozesse vollständig virtuell entwerfen, synthetische Trainingsdaten erzeugen und KI-Modelle bereits vor der Inbetriebnahme trainieren. ABB setzt dabei auf seinen Virtual Controller, der die reale Steuerungssoftware exakt abbildet, sowie auf Absolute-Accuracy-Technologie mit Positioniergenauigkeiten bis 0,5 Millimeter. Ein zentrales Element der Kooperation ist Robot Studio Hyper Reality, das ab der zweiten Jahreshälfte 2026 verfügbar sein soll. Die Plattform soll Einrichtungszeiten um bis zu 80 Prozent reduzieren, Kosten um bis zu 40 Prozent senken und die Markteinführung neuer Produkte um bis zu 50 Prozent beschleunigen. Erste Pilotanwendungen laufen bereits, unter anderem bei Foxconn in der Montage von Unterhaltungselektronik, wo Roboterprozesse zunächst vollständig virtuell optimiert werden. Parallel prüfen ABB und Nvidia die Integration von Edge-KI über die Jetson-Plattform in ABB-Steuerungen, um KI-Funktionen direkt am Roboter auszuführen. Auf der Nvidia GTC zeigt zudem das Robotikunternehmen WORKR Anwendungen, die auf synthetischen Daten trainierte ABB-Roboter nutzen, um insbesondere kleinen und mittleren Herstellern Automatisierung ohne Programmieraufwand zu ermöglichen und dem Fachkräftemangel entgegenzuwirken. (ID:50783089) Bitte geben Sie eine gültige E-Mailadresse ein. Mit Klick auf „Newsletter abonnieren“ erkläre ich mich mit der Verarbeitung und Nutzung meiner Daten gemäß Einwilligungserklärung (bitte aufklappen für Details) einverstanden und akzeptiere die Nutzungsbedingungen. Weitere Informationen finde ich in unserer Datenschutzerklärung. Die Einwilligungserklärung bezieht sich u. a. auf die Zusendung von redaktionellen Newslettern per E-Mail und auf den Datenabgleich zu Marketingzwecken mit ausgewählten Werbepartnern (z. B. LinkedIn, Google, Meta). Stand: 08.12.2025 Es ist für uns eine Selbstverständlichkeit, dass wir verantwortungsvoll mit Ihren personenbezogenen Daten umgehen. 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| Naver to develop Arabic-based LLM, expand AI cooperation with Saudi … | https://www.koreaherald.com/view.php?ud… | 1 | Mar 13, 2026 16:00 | active | |
Naver to develop Arabic-based LLM, expand AI cooperation with Saudi Arabia - The Korea HeraldURL: https://www.koreaherald.com/view.php?ud=20240913050188 Description: Naver Corp., the operator of South Korea's largest internet platform, has signed an initial agreement with Saudi Arabia's artificial intellige Content:
Business Naver to develop Arabic-based LLM, expand AI cooperation with Saudi Arabia Published : Sept. 13, 2024 - 10:20:12 Link copied! 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. (Yonhap) PM Kim Min-seok meets Vance in Washington to discuss trade, investment plan South Korean Prime Minister Kim Min-seok met US Vice President JD Vance in Washington on Thursday, where the two discussed bilateral trade issues, including the implementation of a Korea-US investment framework agreed upon by President Lee Jae Myung and US President Donald Trump last year. Police raid Transport Ministry over Jeju Air plane crash disaster Major roads in central Seoul to be closed Sunday for 2026 Seoul Marathon Is a new K-pop power map emerging in 2026? Ghanaian companies ready to partner Korean firms: President Mahama Korea’s first dating show for the disabled challenges genre with tender, humanistic lens Herald Review Diplomat highlights growing Korean investments in India Diplomatic Circuit Celeb Reads The books celebrities love — and the ones that might become your next read 100 Food Challenge 100 foods to try: Are you up to the challenge? Oddities From the funny to the strange and downright unbelievable Herald Interview A series of in-depth interviews. After DeepSeek: China shifts from breakthrough to AI scale Lee Jae Myung’s war on housing speculation Kia unveils upgraded Niro hybrid, discontinues EV version Hyundai Motor tops Volkswagen in profit for first time Kospi plunges, won weakens as oil shock rattles markets South Korea investigates BTS ticket scalping after nearly 1,900 illegal listings detected Leaving a teaching job in Korea? Here's what foreign instructors need to know The people's king: How "The King's Warden" became Korea's biggest film in years Korea moves to curb 'excessive' celebrity security disrupting airport passenger flow Father of newborn saves five lives through organ donation Address : Huam-ro 4-gil 10, Yongsan-gu,Seoul, Korea Tel : +82-2-727-0114 Online newspaper registration No : Seoul 아03711 Date of registration : 2015.04.28 Publisher. Editor : Choi Jin-Young Juvenile Protection Manager : Choi He-suk The Korea Herald by Herald Corporation. Copyright Herald Corporation. All Rights Reserved.
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| 3 Robots walk into a room ... - RubyFlow | https://rubyflow.com/p/i57q9f-3-robots-… | 1 | Mar 13, 2026 16:00 | active | |
3 Robots walk into a room ... - RubyFlowURL: https://rubyflow.com/p/i57q9f-3-robots-walk-into-a-room- Description: I call my LLM-based service objects robots instead of agents. Why? because robots do what you tell them. Agent have agency. That means they can make choices and do whatever they want to do. Like travel agents, real-estate agents, FBI agents. Robots are machines that follow instructions. Thats what I want. My objects should follow my instructions but what happens when you add a little bit of agency to 3 robots and put them into a room with a set of tools that allow them to communicate with eah other through shared memory, broadcast message channels and direct message channels. Then you tell all 3 robots to do the same thing? They become a self-organizing group. SOGs have agency. Content:
Made a library? Written a blog post? Found a useful tutorial? Share it with the Ruby community here or just enjoy what everyone else has found! I call my LLM-based service objects robots instead of agents. Why? because robots do what you tell them. Agent have agency. That means they can make choices and do whatever they want to do. Like travel agents, real-estate agents, FBI agents. Robots are machines that follow instructions. That’s what I want. My objects should follow my instructions… but what happens when you add a little bit of agency to 3 robots and put them into a room with a set of tools that allow them to communicate with eah other through shared memory, broadcast message channels and direct message channels. Then you tell all 3 robots to do the same thing? They become a self-organizing group. SOGs have agency. https://madbomber.github.io/blog/engineering/robotlab-and-the-writers-room/
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| XGSynBot Debuts Z1 Wheeled Robot, Targeting the "Last Mile" of … | https://moneycompass.com.my/xgsynbot-de… | 1 | Mar 12, 2026 16:01 | active | |
XGSynBot Debuts Z1 Wheeled Robot, Targeting the "Last Mile" of Industrial Embodied AI - Money CompassDescription: Money Compass is one of the credible Chinese and English financial media in Malaysia with strong influence in Malaysia’s financial industry. As the winner of the SME Award in Malaysia for 5 consecutive years, we persistently propel the financial industry towards a mutually beneficial framework. Since 2004, with the dedication to advocating the public to practice financial planning in everyday life, Money Compass has accumulated a vast connection in ASEAN financial industries and garnered government agencies and corporate resources. At present, Money Compass is adjusting its pace to transform into Money Compass 2.0. Consolidating the existing connections and network, Money Compass Integrated Media Platform is founded, which is well grounded in Malaysia whilst serving the ASEAN region. The mission of the new Money Compass Integrated Media Platform is to become the financial freedom gateway to assist internet users enhance financial intelligence, create wealth opportunities and achieve financial freedom for everyone! Content:
BEIJING and SAN FRANCISCO, March 10, 2026 /PRNewswire/ — March 5, 2026, XGSynBot a pioneer in embodied AI, hosted its 2026 product dual-city launch event themed “More Than One Answer” in both Silicon Valley and Beijing. The company officially debuted the Z1 wheeled humanoid robot with the world’s first Modular-End-Effector Quick Change System and self-developed XG- High-Performance Joint Modules. Beyond the product, XGSynBot announced the ” STARFIRE”, a global ecosystem cooperation strategy designed to accelerate the transition of Embodied AI to the real unpredictable, heavy-duty industrial production environment. The event successfully captured the attention of numerous strategic partners and investment institutions, generating potential interests and orders worth tens millions. The Automation Paradox in Manufacturing The global manufacturing sector currently faces a “double-bind”: high-cost automation that remains frustratingly rigid. While the industry has seen a surge in agile humanoid prototypes, few can withstand the 24/7 rigors, oil-splattered environments, and micron-level precision required in actual factories. “We’ve built the world’s most flexible robots over the past three years, yet they remain trapped in the world’s most rigid processes,” said the CEO of XGSynBot “The Z1 isn’t a ‘mascot’ build for the lab; it’s a ‘blue-collar worker’ designed for the real world from the first day.” A Robot Build for the Factory Production At the core of Z1 is a set of hardware and software system architecture decisions intended to prioritize reliability and adaptability in production environments. Modular Quick-Change System: Breaking the limitation of single-purpose robotics, Z1 can switch between different end-effectors—such as grippers, welders, or suction cups in just under 6 seconds, enabling one robot to cover multiple specialized workstations. XG- High-Performance Joint Modules: By integrating motors, reducers, and sensors into a single unit, it significantly improving joint precision, stability, and structural rigidity while eliminating signal interference and latency common in distributed architectures. In practical terms, this means the system is more stable, faster, and built to withstand demanding industrial use. The “Dual-System” Central Brain: Inspired by human cognition, the Z1 features a “Slow System” for high-level task planning and natural language understanding (Reasoning), and a “Fast System” operating at 100Hz for real-time motor control and tactile feedback (Reflex). This allows the robot to understand complex human commands while maintaining millisecond-level stability on the assembly line. STARFIRE: Building an Embodied AI Cooperation Ecosystem Alongside the launch of Z1, XGSynBot announced Project STARFIRE, an initiative aimed at building an open cooperation ecosystem around embodied AI. The program will focus on three areas: Scenario Co-Innovation: Deploying large-scale solutions across 3C electronics, automotive, and renewable energy sectors with global industry partners. Product Synergy: Opening hardware interfaces to third-party tool and component manufacturers to create a “Plug-and-Play” industrial ecosystem. Open-Sourcing: Incrementally open-sourcing proprietary datasets, scenario models, and SDKs in a phased manner, collaborating with academic and industry developers to optimize Embodied AI together. The Bigger Picture for Embodied AI The launch comes at a moment when embodied AI is attracting significant global attention and investment, with startups and large tech companies alike racing to bring intelligent robots into physical workplaces. Yet despite rapid progress in AI models, commercial deployment remains the industry’s biggest hurdle. By focusing on durability, modularity, and ecosystem development, XGSynBot is betting that the next wave of robotics innovation will be defined less by flashy prototypes—and more by machines that can quietly survive the realities of manufacturing production. About XGSynBot XGSynBot is an innovative technology company with cutting-edge AI and robotics. Guided by the core philosophy “Evolve from Unified Architecture. Grow beyond All Limits”, it hopes to empower industries worldwide, redefine human-robot collaboration, and pioneer a new era of productivity. By developing embodied AI robots, XGSynBot is actively advancing robots from single-task to cross-scenario applications. Your email address will not be published. Required fields are marked * Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved Login to your account below Remember Me Please enter your username or email address to reset your password. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved
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| World Internet Conference hosts forum on embodied AI in Spain … | https://bubblear.com/world-internet-con… | 1 | Mar 12, 2026 16:01 | active | |
World Internet Conference hosts forum on embodied AI in Spain – The BubbleURL: https://bubblear.com/world-internet-conference-hosts-forum-on-embodied-ai-in-spain/28298/ Content:
BEIJING, March 7, 2026 /PRNewswire/ — A report by CRI Online: The World Internet Conference Specialized Committee on Artificial Intelligence (the WIC SC on AI) hosted a special forum under the theme “Embodied AI: Leading a New Paradigm of AI Development” on March 3 in Barcelona, during the Mobile World Congress. Notable figures addressing the event included Francis Gurry, vice-chair of the WIC and former director-general of the World Intellectual Property Organization; Mohamed Ben Amor, director general of the Arab Information and Communication Technologies Organization; and Lara Dewar, chief marketing officer of the GSMA. Others who gave speeches were Schahram Dustdar, co-chair of the WIC SC on AI, a member of Academia Europaea and president of the International Artificial Intelligence Industry Alliance; Zhang Dong, executive vice president of China Mobile; Nakul Duggal, executive vice president and group general manager at Qualcomm Technologies, Inc.; and Wang Xiang, senior vice president at ZTE. The forum also featured a roundtable moderated by John Higgins, co-lead of the Standards Program of the WIC SC on AI and chairman of the International AI Governance Association. It was participated by Liu Dong, director of China Future Internet Engineering Center and Internet Hall of Fame Inductee; Jayne Stancavage, vice president of policy and regulatory affairs at Intel Corporation; Emanuela Girardi, president of ADRA; and Qu Zhenbin, chief solutions architect for AI+ at Alibaba Cloud. They discussed the opportunities and challenges emerging in the embodied AI industry. The participants agreed that embodied AI is rapidly transitioning from research and development to large-scale implementation and is now a key focus in AI development. They stressed the importance of deepening international cooperation to foster a healthy, diverse and inclusive global industry ecosystem and build a secure collaborative governance framework. These efforts are crucial to developing safe and reliable embodied AI and ensuring technological progress benefits humanity, they noted. They expressed the hope that the WIC SC on AI would continue to play a vital role in building global consensus and create a shared, intelligent future. The forum was attended by over 100 representatives, including WIC SC on AI members, WIC members and representatives from international organizations, government agencies and institutions in the embodied AI field. View original content to download multimedia:https://www.prnewswire.com/news-releases/world-internet-conference-hosts-forum-on-embodied-ai-in-spain-302707539.html SOURCE CRI Online Disclaimer: The above press release comes to you under an arrangement with PR Newswire. Bubblear.com takes no editorial responsibility for the same. © 2026 - The Bubble. All Rights Reserved.
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| World Internet Conference hosts forum on embodied AI in Spain … | https://moneycompass.com.my/world-inter… | 1 | Mar 12, 2026 16:01 | active | |
World Internet Conference hosts forum on embodied AI in Spain - Money CompassURL: https://moneycompass.com.my/world-internet-conference-hosts-forum-on-embodied-ai-in-spain/ Description: Money Compass is one of the credible Chinese and English financial media in Malaysia with strong influence in Malaysia’s financial industry. As the winner of the SME Award in Malaysia for 5 consecutive years, we persistently propel the financial industry towards a mutually beneficial framework. Since 2004, with the dedication to advocating the public to practice financial planning in everyday life, Money Compass has accumulated a vast connection in ASEAN financial industries and garnered government agencies and corporate resources. At present, Money Compass is adjusting its pace to transform into Money Compass 2.0. Consolidating the existing connections and network, Money Compass Integrated Media Platform is founded, which is well grounded in Malaysia whilst serving the ASEAN region. The mission of the new Money Compass Integrated Media Platform is to become the financial freedom gateway to assist internet users enhance financial intelligence, create wealth opportunities and achieve financial freedom for everyone! Content:
BEIJING, March 8, 2026 /PRNewswire/ — A report by CRI Online: The World Internet Conference Specialized Committee on Artificial Intelligence (the WIC SC on AI) hosted a special forum under the theme “Embodied AI: Leading a New Paradigm of AI Development” on March 3 in Barcelona, during the Mobile World Congress. Notable figures addressing the event included Francis Gurry, vice-chair of the WIC and former director-general of the World Intellectual Property Organization; Mohamed Ben Amor, director general of the Arab Information and Communication Technologies Organization; and Lara Dewar, chief marketing officer of the GSMA. Others who gave speeches were Schahram Dustdar, co-chair of the WIC SC on AI, a member of Academia Europaea and president of the International Artificial Intelligence Industry Alliance; Zhang Dong, executive vice president of China Mobile; Nakul Duggal, executive vice president and group general manager at Qualcomm Technologies, Inc.; and Wang Xiang, senior vice president at ZTE. The forum also featured a roundtable The forum also featured a roundtable moderated by John Higgins, co-lead of the Standards Program of the WIC SC on AI and chairman of the International AI Governance Association. It was participated by Liu Dong, director of China Future Internet Engineering Center and Internet Hall of Fame Inductee; Jayne Stancavage, vice president of policy and regulatory affairs at Intel Corporation; Emanuela Girardi, president of ADRA; and Qu Zhenbin, chief solutions architect for AI+ at Alibaba Cloud. They discussed the opportunities and challenges emerging in the embodied AI industry. The participants agreed that embodied AI is rapidly transitioning from research and development to large-scale implementation and is now a key focus in AI development. They stressed the importance of deepening international cooperation to foster a healthy, diverse and inclusive global industry ecosystem and build a secure collaborative governance framework. These efforts are crucial to developing safe and reliable embodied AI and ensuring technological progress benefits humanity, they noted. They expressed the hope that the WIC SC on AI would continue to play a vital role in building global consensus and create a shared, intelligent future. The forum was attended by over 100 representatives, including WIC SC on AI members, WIC members and representatives from international organizations, government agencies and institutions in the embodied AI field. Your email address will not be published. Required fields are marked * Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved Login to your account below Remember Me Please enter your username or email address to reset your password. Copyright © 2024 Money Compass Media (M) Sdn Bhd. All Rights Reserved
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| Embodied AI Is Leaving the Screen and Entering the Real … | https://www.cmswire.com/digital-experie… | 1 | Mar 12, 2026 16:01 | active | |
Embodied AI Is Leaving the Screen and Entering the Real WorldDescription: As AI moves into robots and physical systems, leaders must rethink operations, workforce models and how intelligence shapes real-world work. Content:
CMSWire's Marketing & Customer Experience Leadership channel is the go-to hub for actionable research, editorial and opinion for CMOs, aspiring CMOs and today's customer experience innovators. Our dedicated editorial and research teams focus on bringing you the data and information you need to navigate today's complex customer, organizational and technical landscapes. We've got you covered. For the past few years, many of our conversations about artificial intelligence (AI) have happened on screens. We've talked to AI, prompted it, queried it and watched it generate text, images and code at remarkable speed. But something important is changing. AI is leaving the chat and entering the physical world. Intelligence is increasingly stepping out of two-dimensional interfaces and into environments where it can move, sense, lift, navigate and assist. This shift toward embodied AI may be one of the clearest signals yet of where the next frontier of AI value could emerge. Embodied AI refers to AI paired with a physical form. That includes robots, autonomous machines and smart systems that can perceive their surroundings, reason about what they're sensing, and take autonomous action in the real world. Embodied AI is already showing up across warehouses, hospitals, factories, logistics networks and agricultural operations. These environments carry real constraints, safety considerations and economic consequences, making them a meaningful proving ground for what comes next. Once AI can see, move and act, the conversation around value, risk and advantage begins to shift. Productivity is no longer limited to faster analysis or better recommendations and instead becomes about physical outcomes such as throughput, safety, uptime and resilience. One way to understand this moment is to recognize that AI is increasingly becoming an ingredient rather than a standalone technology. Many of the more meaningful breakthroughs today are not "AI-only" solutions but "AI and…" – new innovations where AI merges with other technologies. Embodied AI is a key proof point for how machine intelligence today is being woven into the fabric of how work happens, not just how decisions are made. This shift can also reframe AI strategy. Instead of asking where AI can be deployed, leaders may find more impact by asking where intelligence should live inside their operations. In many cases, the answers point beyond software teams and into the physical core of the business. Robotics is one of the clearest signals of embodied AI's momentum. Robots themselves are not new, but the intelligence inside them — as well as their physical abilities to replicate finer human motor skills — is advancing rapidly. Progress in perception, multimodal models, reinforcement learning and edge computing is enabling machines to operate in less structured environments and adapt to variability. That evolution is moving robotics away from rigid automation toward systems that can respond to change and work more fluidly alongside people. What stands out is the level of sustained investment behind these capabilities. According to the International Federation of Robotics, 542,000 industrial robots were installed worldwide in 2024 – more than double the number a decade ago – suggesting that organizations are moving beyond experimentation and into execution, even if adoption varies by sector and use case. Healthcare offers a particularly clear example of this shift. From surgical robotics and rehabilitation systems to autonomous logistics and patient-support technologies, embodied intelligence is increasingly present in clinical and operational settings. These environments demand high levels of trust, precision, and reliability, which can slow adoption, but also sharpen the value proposition when systems perform as intended. Recent industry showcases have highlighted the breadth of innovation underway, and while many solutions are still maturing, the range of use cases suggests embodied AI could play a meaningful role in addressing workforce shortages, operational strain and customer experience challenges over time. Unlike software-based AI, embodied AI often scales more slowly. Hardware constraints, integration complexity, safety requirements and regulatory considerations introduce friction that can temper deployment speed. Yet slower scale does not necessarily mean lower impact. Embodied AI influences parts of the business that software AI rarely touches, including capital investment, labor models, and facility design. Success is not measured solely by efficiency gains. It may appear in the form of fewer workplace injuries, better asset utilization, or more resilient supply chains. When embodied AI is treated as a future concern or delegated entirely to technical teams, leaders may underestimate what is already beginning to shift. The risk is not falling behind on experimentation but overlooking how physical intelligence could reshape the operating model itself. Leaders who wait may miss: Embodied AI is moving beyond software interfaces into physical systems that sense, move and act. The following examples illustrate how machine intelligence is beginning to reshape operations across multiple sectors. Embodied AI signals that AI's impact extends well beyond digital productivity gains. As intelligence moves into physical systems, it begins to influence operations, supply chains, safety protocols, labor dynamics and long-term capital planning. That raises new questions around governance and responsibility — not just about what AI decides, but about what it does in the world. For many leaders, this moment is less about predicting exactly how embodied AI will unfold and more about recognizing the signal. AI is no longer confined to models and interfaces. It is becoming part of the physical fabric of work. Organizations that take the time to design for that reality, thoughtfully and deliberately, can be better positioned to navigate both the opportunities and the risks that follow. Understanding how agentic customer experience and agentic marketing are evolving alongside physical AI systems will be essential for leaders seeking to integrate intelligence across both digital and physical touchpoints. Learn how you can join our contributor community. As the US and global chief AI engineering officer, Scott is in charge of PwC’s cutting-edge technology development in areas that are essential for future innovation development. With 30 years of emerging technology and AI experience, he has helped clients transform their customer experience and enhance digital operations across all aspects of their business. Connect with Scott Likens: For over two decades CMSWire, produced by Simpler Media Group, has been the world's leading community of customer experience professionals. Today the CMSWire community consists of over 5 million influential customer experience, customer service and digital experience leaders, the majority of whom are based in North America and employed by medium to large organizations. Our sister community, Reworked, covers employee experience news and digital workplace news and gathers the world's leading employee experience and digital workplace professionals. And our newest community, VKTR, covers enterprise AI news and is home for AI-focused professionals building agentic AI, prompt engineering and enterprise AI skills, and tracking the top AI chip companies. Not yet a CMSWire member? We serve over 5 million of the world's top customer experience practitioners. Join us today — unlock member benefits and accelerate your career, all for free. For over two decades CMSWire, produced by Simpler Media Group, has been the world's leading community of customer experience professionals. Today the CMSWire community consists of over 5 million influential customer experience, customer service and digital experience leaders, the majority of whom are based in North America and employed by medium to large organizations. Our sister community, Reworked, covers employee experience news and digital workplace news and gathers the world's leading employee experience and digital workplace professionals. And our newest community, VKTR, covers enterprise AI news and is home for AI-focused professionals building agentic AI, prompt engineering and enterprise AI skills, and tracking the top AI chip companies.
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| ZTE CSO Wang Xiang highlights embodied AI at MWC 2026 … | https://www.theregister.com/2026/03/11/… | 1 | Mar 12, 2026 16:01 | active | |
ZTE CSO Wang Xiang highlights embodied AI at MWC 2026 • The RegisterURL: https://www.theregister.com/2026/03/11/zte-cso-highlights-embodied-ai-pathways-mwc-2026/ Description: Partner Content: Wang Xiang details ZTE’s Point-Line-Plane approach to accelerate embodied AI from demonstration to productivity Content:
Partner Content ZTE Corporation (0763.HK / 000063.SZ), a global leading provider of integrated information and communication technology solutions, announced that Wang Xiang, Senior Vice President and Chief Strategy Officer delivered a keynote at World Internet Conference (WIC) during MWC Barcelona 2026, outlining ZTE's vision for embodied AI and its industrialization pathway. In his keynote speech titled "Human–Machine Symbiosis, Intelligence Igniting a New Journey", Wang Xiang presented ZTE's systematic approach to embodied AI, built on the synergy of Sensing, Communication, Computing, AI, and Control and the "Point-Line-Plane" deployment path pioneered at the company's Binjiang manufacturing base. He underscored how cost and efficiency optimization are driving the industry toward scaled commercialization. zte-cso - Click to enlarge Wang Xiang pointed out that AI is undergoing a critical paradigm shift from generative AI to physical AI, evolving from "disembodied" intelligence in the digital world to "embodied" intelligence deeply integrated with the physical world. Embodied AI, as the optimal vehicle for this transformation, is advancing beyond technical validation into scaled commercialization, emerging as the core driver of the physical AI era. It is not only the bridge connecting digital intelligence with the physical environment, but also a source of enormous value across industrial manufacturing, logistics, services, and other sectors, with the market projected to exceed USD 40 billion by 2030. Yet the path to scaled commercialization is not without obstacles. Wang Xiang noted that the complexity and precision of industrial scenarios present three major hurdles: the difficulty of agile single-point deployment in dynamic workstations, the challenge of coordinating heterogeneous agents into efficient production lines, and the lack of seamless integration between intelligent production lines and enterprise business operations. In practice, this means that enabling a single robot to operate quickly and flexibly in dynamic environments remains prohibitively costly; coordinating heterogeneous clusters of robots efficiently faces real-time challenges in communication and scheduling; and allowing intelligent production line data to truly drive business decision-making is hindered by significant delays and breakpoints. Only by overcoming these barriers can embodied AI evolve from "toy" to "tool", from "demonstration" to "productivity", and achieve the leap from "point" to "chain". In response to these systemic challenges, Wang Xiang explained that ZTE has taken its Binjiang manufacturing base—China's first "5G Fully Connected Factory"—as a practical blueprint to explore the "Point-Line-Plane" pathway for advancing Embodied AI applications. Through functional optimization and flexible combination, ZTE extends deployment from a single industry to replication across many sectors, thereby accelerating the commercialization and maturity of Embodied AI. At the "point" level, ZTE synergizes Sensing, Communication, Computing, AI, and Control to create embodied single-unit applications. With full-stack solutions integrating in-house R&D and ecosystem collaboration, these five core elements are standardized, modularized, and made plug-and-play, enabling rapid assembly of versatile embodied agents for key scenarios, addressing specific tasks such as material handling, equipment inspection, and quality testing. At the same time, by combining the proprietary automotive-grade SoC Lanyue A1 with the industrial-grade OS OpenNewStar, ZTE achieves SoC & OS synergy, delivering 4x faster picking speed and advanced motion control capabilities to ensure deterministic and efficient perception-decision-execution loops. In addition, the full-stack simulation platform helps overcome data scarcity, enabling algorithms to migrate rapidly from simulation to real-world deployment. At the "line" level, the company leverages its Co-Sight Super AI Agent as a powerful orchestration tool to seamlessly connect the intelligent nodes with diverse functions. Drag-and-drop orchestration enables minute-level application building, solving collaboration challenges among heterogeneous agents and forming production lines tailored to specific products. At the "plane" level, ZTE relies on its Digital Nebula 4.0 platform, which provides rich large-model capabilities, one-stop development tools, and a transactional architecture. By aggregating more than 100 applications, it enables integrated factory business flow design that links production, logistics, quality and other end-to-end data flows. This allows intelligent production lines to be deeply embedded into enterprise workflows, driving real-time decision-making. This model has already delivered measurable impact. At the Binjiang base, per-capita output value rose 81%, production line adjustment cycles shortened by 20%, and delivery cycles fell 30%. The approach has since been replicated across more than 18 industries—including metallurgy, mining, transportation, power, and ports—working with over 1,000 partners to create 24+ product categories and 100+ scenario applications. In doing so, ZTE has embedded embodied intelligence across thousands of sectors, accelerating its transformation from demonstration to productivity. Wang Xiang highlighted that to support this pathway, ZTE has consistently built core competitiveness around five dimensions: Sensing, Communication, Computing, AI, and Control. The company adheres to a balanced approach of proprietary R&D and ecosystem partnership, combining ultimate synergy with open innovation. In sensing and communication, ZTE leverages an internet platform + MEC to widely integrate ecosystem sensors, while advanced 5G/5G-A, MEC, and OTN technologies deliver end-to-end deterministic connectivity, building the "highways" and "neural networks" for agents. In computing and AI, ZTE provides an open computing hardware foundation that supports diverse, on-demand compute, achieving five-dimensional synergy across chip, standard, architecture, algorithm, and delivery. The "1+N+X" large model portfolio spans foundational models, domain models, and applications, lowering adoption barriers across industries. In control and orchestration, hardware-software synergy strengthens motion control, while the Co-Sight Super AI Agent and Digital Nebula platform enable rapid orchestration and seamless integration of embodied intelligence into enterprise operations. Wang Xiang stressed that the advancement of embodied AI is not the endeavor of a single enterprise but a collective effort across the industry. Positioned as both a core technology innovator and an ecosystem enabler, ZTE will continue to drive independent innovation in key technologies while promoting standardized hardware, platform software, modular solutions, and open ecosystem. Working hand-in-hand with global partners, ZTE is committed to strengthening foundational technologies, fostering open collaboration, and ushering in a new journey of human-machine symbiosis to co-create a brighter digital future. Contributed by ZTE. Send us news The Register Biting the hand that feeds IT Copyright. All rights reserved © 1998–2026
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| Deep Reinforcement Learning — DQN (Part 2) | https://medium.com/@ojescobar14/deep-re… | 0 | Mar 11, 2026 16:00 | active | |
Deep Reinforcement Learning — DQN (Part 2)URL: https://medium.com/@ojescobar14/deep-reinforcement-learning-dqn-part-2-f34e016d40b7 Description: Deep Reinforcement Learning — DQN (Part 2) Preface: As I started to write the next article in this series, one thing became apparent: giving background and ex... Content: |
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| How can robots acquire skills through interactions with the physical … | https://robohub.org/how-can-robots-acqu… | 1 | Mar 11, 2026 16:00 | active | |
How can robots acquire skills through interactions with the physical world? An interview with Jiaheng Hu - RobohubContent:
One of the key challenges in building robots for household or industrial settings is the need to master the control of high-degree-of-freedom systems such as mobile manipulators. Reinforcement learning has been a promising avenue for acquiring robot control policies, however, scaling to complex systems has proved tricky. In their work SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL, Jiaheng Hu, Peter Stone and Roberto Martín-Martín introduce a method that renders real-world reinforcement learning feasible for complex embodiments. We caught up with Jiaheng to find out more. This paper is about how robots (in particular, household robots like mobile manipulators) can autonomously acquire skills via interacting with the physical world (i.e. real-world reinforcement learning). Reinforcement learning (RL) is a general learning framework for learning from trial-and-error interaction with an environment, and has huge potential in allowing robots to learn tasks without humans hand-engineering the solution. RL for robotics is a very exciting field, as it can open possibilities for robots to self-improve in a scalable way, towards the creation of general-purpose household robots that can assist people in our everyday lives. Previously, most of the successful applications of RL to robotics were done by training entirely in simulation, then deploying the policy in the real-world directly (i.e. zero-shot sim2real). However, such a method has big limitations: on one hand, it is not very scalable, as you need to create task-specific, high-fidelity simulation environments that highly match the real-world environment that you want to deploy the robot in, and this can often take days or months for each and every task. On the other hand, some tasks are actually very hard to simulate, as they involve deformable objects and contact-rich interactions (for example, pouring water, folding clothes, wiping whiteboard). For these tasks, the simulation is often quite different from the real world. This is where real-world RL comes into play: if we can allow a robot to learn by directly interacting with the physical world, we don’t need a simulator anymore. However, while several attempts have been made towards realizing real-world RL, it is actually a very hard problem since: 1. Sample-inefficiency: RL requires a lot of samples (i.e. interaction with the environment) to learn good behavior, which is often impossible to collect in large quantities in the real-world. 2. Safety Issues: RL requires exploration, and random exploration in the real-world is often very very dangerous. The robot can break itself and will never be able to recover from that. So, creating high-fidelity simulations is very hard, and directly learning in the real-world is also really hard. What should we do? The key idea of SLAC is that we can use a low-fidelity simulation environment to assist subsequent real-world RL. Specifically, SLAC implements this idea in a two-step process: in the first step, SLAC learns a latent action space in simulation via unsupervised reinforcement learning. Unsupervised RL is a technique that allows the robot to explore a given environment and learn task-agnostic behaviors. In SLAC, we design a special unsupervised RL objective that encourages these behaviors to be safe and structured. In the second step, we treat these learned behaviors as the new action space of the robot, where the robot does real-world RL for downstream tasks such as wiping whiteboards by making decisions in this new action space. Importantly, this method allow us to circumvent the two biggest problem of real-world RL: we don’t have to worry about safety issues since the new action space is pretrained to be always safe; and we can learn in a sample-efficient way because our new action space is trained to be very structured. The robot carrying out the task of wiping a whiteboard. We test our methods on a real Tiago robot – a high degrees-of-freedom, bi-manual mobile manipulation, on a series of very challenging real-world tasks, including wiping a large whiteboard, cleaning a table, and sweeping trash into a bag. These tasks are challenging from three aspects: 1. They are visuo-motor tasks that require processing of high-dimensional image information. 2. They require the whole-body motion of the robot (i.e. controlling many degrees-of-freedom at the same time), and 3. They are contact-rich, which makes it hard to simulate accurately. On all of these tasks, our method allows us to learn high-performance policies (>80% success rate) within an hour of real-world interactions. By comparison, previous methods simply cannot solve the task, and often risk breaking the robot. So to summarize, previously it was simply not possible to solve these tasks via real-world RL, and our method has made it possible. I think there is still a lot more to do at the intersection of RL and robotics. My eventual goal is to create truly self-improving robots that can learn entirely by themselves without any human involvement. More recently, I’ve been interested in how we can leverage foundation models such as vision-language models (VLMs) and vision-language-action models (VLAs) to further automate the self-improvement loop. Jiaheng Hu is a 4th-year PhD student at UT-Austin, co-advised by Prof. Peter Stone and Prof. Roberto Martín-Martín. His research interest is in Robot Learning and Reinforcement Learning, with the long-term goal of developing self-improving robots that can learn and adapt autonomously in unstructured environments. Jiaheng’s work has been published at top-tier Robotics and ML venues, including CoRL, NeurIPS, RSS, and ICRA, and has earned multiple best paper nominations and awards. During his PhD, he interned at Google DeepMind and Ai2, and is a recipient of the Two Sigma PhD Fellowship. SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL, Jiaheng Hu, Peter Stone, Roberto Martín-Martín.
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| Using Adversarial Reinforcement Learning to Improve the Resilience of Human-Robot … | https://ifip.hal.science/hal-05540657v1 | 1 | Mar 11, 2026 16:00 | active | |
Using Adversarial Reinforcement Learning to Improve the Resilience of Human-Robot Collaboration in Industrial Assembly - IFIP Open Digital LibraryURL: https://ifip.hal.science/hal-05540657v1 Description: The paper proposes a novel approach to enhance the resilience of mutual collaborative activity between humans and robots in industrial assembly tasks. The approach exploits Adversarial Reinforcement Learning (ARL) to enable a robot to learn an assembly policy that is robust against human mistakes. The adversary can represent various sources of uncertainty or disturbance in the environment. By learning from adversarial feedback, the agent can improve its performance and adaptability in challenging scenarios. The paper applies ARL to the execution of the assembly task sequence. The robot acts as one agent and learns how to assist the human partner during the assembly. The agent simulating the human partner acts as the adversary and deliberately introduces mistakes during the assembly process. The robot also learns how to cope with different levels of human competence and cooperation by adjusting its own behaviour accordingly. The paper evaluates the proposed approach through experiments reproducing complex assembly sequences and compares it with baseline methods that use conventional optimization algorithms. The results show that ARL does not outperforms conventional optimization algorithms in terms of task completion time but guarantee robustness against human mistakes. The paper also discusses the implications for human-robot collaboration and suggests future directions for research. Content:
The paper proposes a novel approach to enhance the resilience of mutual collaborative activity between humans and robots in industrial assembly tasks. The approach exploits Adversarial Reinforcement Learning (ARL) to enable a robot to learn an assembly policy that is robust against human mistakes. The adversary can represent various sources of uncertainty or disturbance in the environment. By learning from adversarial feedback, the agent can improve its performance and adaptability in challenging scenarios. The paper applies ARL to the execution of the assembly task sequence. The robot acts as one agent and learns how to assist the human partner during the assembly. The agent simulating the human partner acts as the adversary and deliberately introduces mistakes during the assembly process. The robot also learns how to cope with different levels of human competence and cooperation by adjusting its own behaviour accordingly. The paper evaluates the proposed approach through experiments reproducing complex assembly sequences and compares it with baseline methods that use conventional optimization algorithms. The results show that ARL does not outperforms conventional optimization algorithms in terms of task completion time but guarantee robustness against human mistakes. The paper also discusses the implications for human-robot collaboration and suggests future directions for research. Connect in order to contact the contributor https://ifip.hal.science/hal-05540657 Submitted on : Friday, March 6, 2026-4:42:09 PM Last modification on : Friday, March 6, 2026-4:50:40 PM Contact Resources Informations Legal issues Portals CCSD
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| Xiaomi Integrates Humanoid Robots Into Manufacturing Lines | Ubergizmo | https://www.ubergizmo.com/2026/03/xiaom… | 1 | Mar 10, 2026 08:00 | active | |
Xiaomi Integrates Humanoid Robots Into Manufacturing Lines | UbergizmoURL: https://www.ubergizmo.com/2026/03/xiaomi-robots-manufacturing/ Description: Xiaomi has officially transitioned its humanoid robot project from laboratory prototypes to real-world industrial applications. According to CEO Lei... Content:
Xiaomi has officially transitioned its humanoid robot project from laboratory prototypes to real-world industrial applications. According to CEO Lei Jun, these robots are currently undergoing operational testing within the company’s automotive assembly plants, marking a significant step toward full-scale industrial automation. At present, the humanoid models are deployed in specific assembly stations where they perform repetitive yet high-precision tasks. These include transporting material boxes and loading self-tapping nuts. Unlike traditional stationary industrial arms, these robots utilize a “Vision-Language-Action” (VLA) AI model titled Xiaomi-Robotics-0. This system integrates multimodal perception—combining visual data and sensor feedback—with reinforcement learning. This allows the machines to interpret complex instructions and adapt to environmental variables rather than merely following fixed, pre-programmed scripts. The company’s strategic roadmap involves a massive rollout of these units over the next five years. To ensure reliability, Xiaomi is monitoring key performance indicators (KPIs) such as: Mean Time Between Failures (MTBF): Measuring the consistency of hardware and software. Single-Task Success Rate: Evaluating precision in delicate assembly maneuvers. Lei Jun noted that as the software is optimized through continuous experience, the robots are becoming increasingly compatible with large-scale production demands. This initiative reflects Xiaomi’s broader evolution from a smartphone manufacturer into a diversified technology powerhouse spanning electric vehicles, smart home ecosystems, and advanced robotics. By integrating AI-driven humanoids, the company aims to reduce operational costs and gain a competitive edge in supply chain management. However, this shift also mirrors a broader economic trend in China, raising significant questions regarding the long-term impact on the human workforce and the potential displacement of factory laborers by autonomous systems. Filed in Robots >Transportation. Read more about Humanoid Robot, Robotics and Xiaomi.
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| Ethereum Builders Should Focus On 'Sanctuary Tech' Instead Of Trying … | https://finance.yahoo.com/news/ethereum… | 1 | Mar 10, 2026 00:02 | active | |
Ethereum Builders Should Focus On 'Sanctuary Tech' Instead Of Trying 'To Be Apple Or Google,' Vitalik Buterin SaysURL: https://finance.yahoo.com/news/ethereum-builders-focus-sanctuary-tech-172721602.html Description: Ethereum should serve as a space where people can interact free from corporate and government control, co-founder Vitalik Buterin says. "Ethereum should conceptualize ourselves as being part of an ecosystem building ‘sanctuary technologies:' free open-source technologies that let people live,... Content:
Oops, something went wrong Benzinga and Yahoo Finance LLC may earn commission or revenue on some items through the links below. Ethereum should serve as a space where people can interact free from corporate and government control, co-founder Vitalik Buterin says. "Ethereum should conceptualize ourselves as being part of an ecosystem building ‘sanctuary technologies:' free open-source technologies that let people live, work, talk to each other, manage risk and build wealth, and collaborate on shared goals, in a way that optimizes for robustness to outside pressures," Buterin said on X on March 3. Don't Miss: Build your own AI-powered index in minutes — and earn an uncapped 1% match when you move your portfolio to Public. Learn how it works. Maximize saving for your retirement and cut down taxes: Schedule your free call with a financial advisor to start your financial journey – no cost, no obligation. He named Starlink, Signal and X community notes as examples of technologies developers should aspire to while discouraging attempts "to be Apple or Google." He added that developers should build full-stack ecosystems from the wallet to AI-powered interfaces and hardware. The goal is "de-totalization," Buterin said, referring to a scenario where humans are able to avoid a life controlled by a single entity. Buterin’s remarks come as he says people are growing increasingly concerned about control and surveillance by governments and corporations and how AI could interact with them. See Also: Own a Stake in California's New Standard for Luxury Behavioral Health Buterin last month urged developers exploring intersections between Ethereum and AI to focus on use cases that foster human freedoms rather than simply pursuing artificial general intelligence. The developer has become increasingly vocal about Ethereum’s founding ethos in recent months. The timing of his rallying cries coincides with increased institutional adoption of the blockchain and excitement around its potential for tokenization. Read Next: Before the IPO: How One Company Quietly Locked Up 500+ Iconic Character Rights The AI Marketing Platform Backed by Insiders from Google, Meta, and Amazon — Invest at $0.85/Share Building a resilient portfolio means thinking beyond a single asset or market trend. Economic cycles shift, sectors rise and fall, and no one investment performs well in every environment. That's why many investors look to diversify with platforms that provide access to real estate, fixed-income opportunities, professional financial guidance, precious metals, and even self-directed retirement accounts. By spreading exposure across multiple asset classes, it becomes easier to manage risk, capture steady returns, and create long-term wealth that isn't tied to the fortunes of just one company or industry. Rad AI Rad AI's award-winning artificial intelligence technology helps transform data chaos into actionable insights, enabling the creation of high-performing content with measurable ROI. Their Regulation A+ offering allows investors to participate at $0.85 per share with a minimum investment of $1,000, providing an opportunity to diversify portfolios into early-stage AI innovation. For investors seeking exposure to the rapidly growing AI and tech sector, Rad AI offers a chance to get in on the ground floor of a data-driven growth story. Paladin Paladin Power is addressing the growing demand for energy independence with a fire-safe energy storage system that doesn't rely on lithium-ion batteries. Instead, its ESS uses non-lithium, solid-state graphene battery technology designed for durability, safety, and long service life—positioning it as an alternative to fire-prone storage solutions that dominate today's market. Since launching in 2023, Paladin has generated $185 million in contracted revenue, achieved strong year-over-year growth, and secured a manufacturing agreement with NYSE-listed Jabil. With systems already deployed across residential and commercial properties and a $500B global electrification market opportunity ahead, Paladin offers investors exposure to decentralized energy infrastructure backed by real contracts, U.S.-based manufacturing, and scalable next-generation technology. Elf Labs Elf Labs is an IP-focused entertainment company built on a strategy that has powered giants like Disney and Marvel: ownership of globally recognized character IP. After more than a decade of rights acquisition, the company controls 500+ protected trademarks and copyrights tied to iconic characters including Cinderella, Snow White, Rapunzel, Sleeping Beauty, and Peter Pan. This foundation has generated over $15 million in royalties, expanded licensing into 30+ countries, and supported development of 100+ product lines. With its Nasdaq ticker ($ELFS) reserved and valuation growth exceeding 1,600% in under two years, Elf Labs is now scaling distribution through patented production systems, global licensing, and streaming and mobile initiatives—offering investors exposure to a private entertainment company with a clear public-market trajectory. Valley Center Wellness Valley Center Wellness is setting a new benchmark in luxury behavioral health with its flagship facility in Corona, California. Designed as a private, resort-style wellness retreat on a 4.2-acre estate, the center combines discretion, comfort, and comprehensive care, offering patients private chefs, daily massages, acupuncturist sessions, and access to a pool, spa, gym, and basketball court. Focused on high-profile and affluent clients, Valley Wellness provides fully customized treatment plans outside the constraints of insurance, emphasizing long-term recovery, holistic wellness, and life-after-addiction strategies. Through its three-stage care model—including residential, outpatient, and transitional housing—patients experience continuity of care that supports lasting change. For investors, Valley Wellness has launched an equity crowdfunding opportunity, offering a way to participate in a fast-growing $42 billion behavioral health sector while gaining exposure to both high-end real estate and a premium healthcare business. Immersed Immersed is a private, pre-IPO technology company operating at the intersection of AI, spatial computing, and remote work. Best known for building the most widely used productivity app on the Meta Quest platform, Immersed enables professionals and teams to work full-time in shared virtual environments across macOS, Windows, and Linux. The company is expanding beyond software with its own productivity-focused XR headset and AI tools, supported by partnerships with major technology firms including Meta, Samsung, and Qualcomm. Immersed is currently allowing retail investors to participate in its pre-IPO round, subject to eligibility and offering terms. Arrived Backed by Jeff Bezos, Arrived Homes makes real estate investing accessible with a low barrier to entry. Investors can buy fractional shares of single-family rentals and vacation homes starting with as little as $100. This allows everyday investors to diversify into real estate, collect rental income, and build long-term wealth without needing to manage properties directly. Masterworks Masterworks enables investors to diversify into blue-chip art, an alternative asset class with historically low correlation to stocks and bonds. Through fractional ownership of museum-quality works by artists like Banksy, Basquiat, and Picasso, investors gain access without the high costs or complexities of owning art outright. With hundreds of offerings and strong historical exits on select works, Masterworks adds a scarce, globally traded asset to portfolios seeking long-term diversification. Rex Shares REX Shares designs specialized ETFs for investors who want more precision than traditional broad-market funds can offer. Its lineup spans options-based income strategies, leveraged and inverse exposures, spot-linked crypto ETFs, and thematic funds tied to structural trends. By targeting specific income objectives, volatility profiles, or market themes, these ETFs can be used alongside core holdings to introduce differentiated return drivers and reduce reliance on a single market outcome, while maintaining the liquidity and transparency of the ETF structure. Motley Fool Motley Fool Asset Management brings its long-standing "Foolish" investing philosophy into a lineup of passive ETFs designed around clear, rules-based investment styles. Built using decades of proprietary research from The Motley Fool, LLC, these factor-based ETFs focus on growth, value, and momentum strategies, selecting U.S. companies based on quality, risk, and long-term potential. For investors who want professionally vetted stock exposure without the demands of active trading, Motley Fool Asset Management offers a straightforward way to access expert-driven strategies through the simplicity and liquidity of an ETF. Finance Advisors Finance Advisors helps Americans approach retirement with greater clarity by connecting them to vetted, fiduciary financial advisors who specialize in tax-aware retirement planning. Rather than focusing on products or investment performance alone, the platform emphasizes strategies that account for after-tax income, withdrawal sequencing, and long-term tax efficiency—factors that can materially impact retirement outcomes. Free to use, Finance Advisors gives individuals with meaningful savings access to a level of planning sophistication historically reserved for high-net-worth households, helping reduce hidden tax risk and improve long-term financial confidence. Public Public is a multi-asset investing platform built for long-term investors who want more control, transparency, and innovation in how they grow wealth. Founded in 2019 as the first broker-dealer to offer commission-free, real-time fractional investing, Public now lets users invest in stocks, bonds, options, crypto, and more—all in one place. Its latest feature, Generated Assets, uses AI to turn a single idea into a fully customized, investable index that can be explained and backtested before committing capital. Combined with AI-powered research tools, clear explanations of market moves, and an uncapped 1% match for transferring an existing portfolio, Public positions itself as a modern platform designed to help serious investors make more informed decisions with context. Money Pickle Money Pickle helps people connect with vetted fiduciary financial advisors—professionals who are legally obligated to act in their clients' best interests. Through a quick online quiz, users are matched with a fiduciary for a complimentary, no-obligation one-on-one strategy session tailored to goals like retirement planning, investing, tax strategy, or getting financially organized. With no upfront costs and no sales pressure, Money Pickle removes the friction and uncertainty from finding trustworthy advice, making personalized financial guidance accessible whether you're building wealth, preserving it, or planning for the future. Atari Atari is bringing its iconic legacy into the physical world with the launch of the first-ever Atari Hotel, a construction-ready gaming and entertainment destination in downtown Phoenix. The Atari Hotel Phoenix blends immersive gaming, live events, dining, and technology-driven experiences into a next-generation hospitality concept, backed by secured land, licensing, and development partners. Through a Regulation A+ offering, investors can own a direct stake in the land, building, and branded hotel starting at $500, with targeted returns including a 15% preferred return and a projected 5.8x multiple. As gaming and experiential travel continue to converge, this opportunity allows everyday investors to participate alongside developers in transforming a legendary brand into a real-world destination. Image: Shutterstock This article Ethereum Builders Should Focus On 'Sanctuary Tech' Instead Of Trying 'To Be Apple Or Google,' Vitalik Buterin Says originally appeared on Benzinga.com © 2026 Benzinga.com. Benzinga does not provide investment advice. All rights reserved. Sign in to access your portfolio
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| NPC Theater AI: Robots Performing Quirky Stories by Georg Haller … | http://www.kicktraq.com/projects/292163… | 10 | Mar 09, 2026 16:01 | active | |
NPC Theater AI: Robots Performing Quirky Stories by Georg Haller :: KicktraqURL: http://www.kicktraq.com/projects/29216330/npc-theater-ai-robots-performing-quirky-stories/ Description: NPC Theater AI — robots stage plays based on your ideas. We want to understand human emotions through art – Join the Mission! Content: Images (10): 
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| $5900 Unitree R1 robot is surprisingly affordable | Fox News | https://www.foxnews.com/tech/5900-unitr… | 1 | Mar 09, 2026 00:01 | active | |
$5900 Unitree R1 robot is surprisingly affordable | Fox NewsURL: https://www.foxnews.com/tech/5900-unitree-r1-robot-surprisingly-affordable Description: Unitree just dropped its latest creation, the R1 humanoid robot, and people are talking. Content:
This material may not be published, broadcast, rewritten, or redistributed. ©2026 FOX News Network, LLC. All rights reserved. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset. Powered and implemented by FactSet Digital Solutions. Legal Statement. Mutual Fund and ETF data provided by LSEG. Industries can rethink how work gets done, raising the bar for productivity and workplace safety. Unitree just dropped its latest creation, the R1 humanoid robot, and people are talking. At only $5,900, it's the most affordable bipedal robot we've seen so far. The low price has taken the tech world by surprise and kicked off a wave of excitement. It's a big step toward making humanoid robots more affordable for people. Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide - free when you join my CYBERGUY.COM/NEWSLETTER. R1 humanoid robot. (Unitree) In Unitree's promo videos, the R1 shows off by running, spinning, shadowboxing, doing handstands, and even nailing cartwheels. People are starting to realize just how far these humanoid robots have come in terms of coordination and agility. What's especially wild is that it's not priced exclusively for big research labs; regular consumers might actually be able to get their hands on one. R1 humanoid robot doing a handstand. (Unitree) The robot can pull off impressive moves thanks to 26 joint degrees of freedom, giving it flexibility similar to a gymnast. It uses onboard sensors, like binocular and wide-angle cameras, microphones, and speakers to understand and navigate its surroundings. An 8-core CPU and GPU power tasks such as voice and image recognition. Its battery lasts about one hour per charge, which is solid for a robot this size. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Speaking of size, the R1 weighs around 55 pounds and stands about 4 feet tall. That makes it compact enough to fit easily into classrooms or labs. The standard model comes with fixed open fists, so it can't actually grip objects. However, an advanced EDU version offers movable fingers and lets each arm carry up to 6.6 pounds. R1 humanoid robot. (Unitree) Unitree's older models include the G1 at sixteen thousand dollars and the H1 at over ninety thousand. In comparison, the R1 feels like a total game changer. Its lower price gives researchers, small developers, and educators a new opportunity to explore humanoid robotics. Of course, some people are a little skeptical. A few have raised questions about whether the promo footage uses CGI or overly scripted setups. And let's be honest, anyone who's seen robots go off-script knows how unpredictable things can get. That's why solid software and strong safety systems are still so important, especially at this price point. R1 humanoid robot running. (Unitree) Administrators and researchers around the world are closely watching Unitree's move. China's strength in manufacturing and low-cost hardware gives it a clear advantage, especially as it goes head-to-head with U.S. players like Tesla, Figure AI, and Agility Robotics. Everyone's racing to make humanoids affordable and practical. GET FOX BUSINESS ON THE GO BY CLICKING HERE Some researchers are already working the R1 into academic projects. Researchers expect machine learning systems and training tools from older models to work with the R1 as well. And in the medical world, some trials are exploring how humanoid robots can assist in remote care, though they still need improvements in strength and sensitivity. Two R1 humanoid robots. (Unitree) If you've ever dreamed of working with a humanoid robot but thought it was out of reach, the R1 changes that. At $5,900, it's affordable enough for educators, researchers, and developers on a budget. It can walk, spin, and even cartwheel, giving you a real platform to test AI and robotics projects. The standard version doesn't grip, but the EDU model adds movable fingers and more power. With its compact size and one-hour battery life, the R1 fits easily into classrooms, labs, or maker spaces. It's not perfect, but it's a big step toward making humanoid robotics truly accessible. CLICK HERE TO GET THE FOX NEWS APP The Unitree R1 is catching attention for all the right reasons. It's fast, flexible, and surprisingly affordable, just $5,900 for a bipedal humanoid that can run, cartwheel, and react to its surroundings. That's huge for schools, researchers, and developers who've never had access to this kind of tech at this kind of price. But while it looks impressive on video, some folks are wondering how it performs in real life. Is it a reliable research tool or just a flashy demo machine? One thing's clear: the R1 could mark a turning point in the push to bring humanoid robots into everyday life. Could robots like this really end up in classrooms, clinics, or even homes someday? If humanoid robots become affordable, how comfortable would you be sharing your space with one? Let us know by writing to us at Cyberguy.com/Contact. Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide - free when you join my CYBERGUY.COM/NEWSLETTER. Copyright 2025 CyberGuy.com. All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com. Get a daily look at what’s developing in science and technology throughout the world. Subscribed You've successfully subscribed to this newsletter! This material may not be published, broadcast, rewritten, or redistributed. ©2026 FOX News Network, LLC. All rights reserved. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset. Powered and implemented by FactSet Digital Solutions. Legal Statement. Mutual Fund and ETF data provided by LSEG.
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| Unitree Technology and JD Open First Offline Store in Beijing … | https://pandaily.com/unitree-technology… | 0 | Mar 09, 2026 00:01 | active | |
Unitree Technology and JD Open First Offline Store in Beijing - PandailyURL: https://pandaily.com/unitree-technology-and-jd-open-first-offline-store-in-beijing Description: Unitree Technology takes its robotics experience offline with a first store in Beijing, letting customers try, buy, and interact with its humanoid and quadruped robots. Content: |
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| WATCH humanoid robot perform kung fu — RT Entertainment | https://www.rt.com/pop-culture/613387-h… | 1 | Mar 09, 2026 00:01 | active | |
WATCH humanoid robot perform kung fu — RT EntertainmentURL: https://www.rt.com/pop-culture/613387-humanoid-robot-kung-fu-video/ Description: A Chinese robotics firm has revealed an upgraded android able to “learn and perform virtually any movement” Content:
Chinese robotics company Unitree has shared a video featuring its humanoid robot doing kung fu moves. The bot’s balance capabilities and range of movement have been upgraded, the firm said. Humanoid robots are made to resemble and act like humans, imitating facial expressions, movements, and speech. The video teaser published by the Hangzhou-based company earlier this week shows the human-like robot walking down the street while performing various martial arts strikes and kicking techniques. Unitree stated that the latest algorithm upgrade allows its G1 humanoid robot to “learn and perform virtually any movement.” Kungfu BOT: Unitree G1🥳We have continued to upgrade the Unitree G1's algorithm, enabling it to learn and perform virtually any movement. What other moves would you like to see. Do share with us in the comments. (Please keep a safe distance from the robot.)#Unitree#Kungfu… pic.twitter.com/1vDZHyRjqZ As per the company’s website, the $16,000 G1 humanoid robot, which debuted in August 2023, features powered joints on its arms, legs, and torso that allow 23 degrees of freedom. Earlier this month, Unitree unveiled video footage of its humanoid G1 and H1 androids showing off new moves. G1, a more affordable version of the robot, was shown running, navigating uneven terrain, and walking in a more natural way. The taller H1 model performed a preset routine alongside human dancers at the Spring Festival Gala event marking the Chinese New Year. A number of companies – including Japan’s Honda, Hyundai Motor’s Boston Dynamics, and Agility Robotics – have been betting on humanoid robots to meet potential labor shortages in certain industries by performing repetitive tasks that may be seen as dangerous or tedious. Tesla, Meta, and OpenAI have recently joined the trend. Earlier this month, Bloomberg cited sources as stating that Meta Platforms is planning to invest into futuristic robots that can act like humans and assist with physical tasks. The company is reportedly forming a new team within its Reality Labs hardware division to conduct the work. Last December, media reports emerged that OpenAI, the creator of ChatGPT, is seeking to develop its own android. Last year, electric-vehicle producer Tesla announced plans to introduce humanoid robots for internal purposes starting in 2025, with plans for broader production by the following year. Valued at $1.8 billion in 2023, the global humanoid robot market is projected to soar to more than $13 billion over the next five years, according to research firm MarketsandMarkets. RT News App © Autonomous Nonprofit Organization “TV-Novosti”, 2005–2026. All rights reserved. This website uses cookies. Read RT Privacy policy to find out more.
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| UPSC GS-3 Mains Answer Writing Practice (16 Jan 2026): Robotics | https://www.insightsonindia.com/2026/01… | 1 | Mar 08, 2026 16:00 | active | |
UPSC GS-3 Mains Answer Writing Practice (16 Jan 2026): RoboticsDescription: UPSC GS-3 Mains Answer Writing Practice (16 Jan 2026): Analyse how advancements in robotics are transforming industrial labour processes. Examine their implications for skill formation. Assess policy challenges arising from this shift. Boost your preparation with structured practice. Content:
Call us @ 08069405205 Topic: Science & Technology Q6. Analyse how advancements in robotics are transforming industrial labour processes. Examine their implications for skill formation. Assess policy challenges arising from this shift. (15 M) Difficulty Level: Medium Reference: InsightsIAS Why the question The accelerating adoption of industrial robotics is reshaping production systems while raising concerns about employment transitions, skill gaps and regulatory readiness. Key Demand of the question The question requires analysing how robotics transforms industrial labour processes, examining its implications for skill formation, and assessing the policy challenges arising from this shift in an integrated manner. Structure of the Answer Introduction Briefly contextualise robotics within Industry 4.0 and its growing role in redefining industrial labour and production efficiency. Body Conclusion Conclude by underscoring the need for anticipatory skilling strategies and adaptive policy frameworks to align robotics-led industrial growth with inclusive development. No Related Posts found Bangalore 3rd Floor, Nanda Ashirwad Building, Chandra Layout Main Rd, Maruthi Nagar, Attiguppe, Bengaluru, Karnataka 560040 Google Map+ Delhi #B-10, 3rd Floor, Bada Bazar Rd, Old Rajinder Nagar, New Delhi, Delhi 110060 Google Map+ Srinagar 3rd Floor, Opposite Hotel Solar Residency, Aramwari, Pathanbagh, Rajbagh, Srinagar-190008 Google Map+ Davanagere #1092 Jadhav Complex, Ring Road, Nijilingappa Layout, opposite to EDU ASIA School Davangere, Karnataka 577004 Insights IAS: Simplifying UPSC IAS Exam Preparation. InsightsIAS has redefined, revolutionized and simplified the way aspirants prepare for UPSC IAS Civil Services Exam. Today, it’s India’s top website and institution when it comes to imparting quality content, guidance and teaching for the IAS Exam. Contact Us: Call us at 080 69405205 (toll-free) support@insightsias.com careers@insightsias.com Copyright © Insights Active Learning
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| Tether Backs $81 Million Humanoid Robotics Project in Italy | https://www.pymnts.com/news/investment-… | 1 | Mar 08, 2026 16:00 | active | |
Tether Backs $81 Million Humanoid Robotics Project in ItalyContent:
Italian robotics firm Generative Bionics has raised $81 million in new funding. Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content — no additional logins required. yesSubscribe to our daily newsletter, PYMNTS Today. By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. Δ The new funding round was one of the largest ever in Europe in the “humanoid robotics deep tech space,” Generative Bionics said in a Monday (Dec. 8) announcement. “Our mission is to build a future where intelligent humanoid robots collaborate daily with people, amplifying human cognitive and physical potential,” said Daniele Pucci, the company’s co-founder and CEO. “Our Physical AI enables us to design and manufacture human-inspired robots that create tangible value across multiple applications.” He pointed to analyses projecting that the humanoid robotics market will exceed 200 billion euros ($232 billion) by 2035 and could top $5 trillion by 2050. “This is an epochal transformation: our goal is to position Generative Bionics as a global leader in physical AI for human-centric humanoid ecosystems,” Pucci said. We’d love to be your preferred source for news. Please add us to your preferred sources list so our news, data and interviews show up in your feed. Thanks! The round was led by CDP Venture Capital, with participation by investors that include Tether. The company says it will use the funding to boost product development, train physical artificial intelligence (AI) systems — what it calls the fusion of robotics and AI — and to construct its first production plant. Advertisement: Scroll to Continue “The company is also finalizing its first industrial deployment contracts, which will be announced in early 2026, marking the introduction of humanoids into real production environments,” the announcement said. As PYMNTS wrote last month, physical AI has emerged as the next phase of robotics as new developments in sensing, perception and large AI models provide machines with capabilities that had never been supported by traditional automation. “Earlier robots followed fixed commands and worked only in predictable environments, struggling with the unpredictability found in everyday operations such as shifting layouts, varying item shapes, mixed lighting, and human movement,” that report said. “That is beginning to change as research groups show how simulation, digital twins and multimodal learning pipelines enable robots to learn adaptive behaviors and carry those behaviors into real facilities with minimal retraining.” An example of this technology in the day-to-day world is Amazon’s launch of its Vulcan robot earlier this year. Vulcan employs both vision and touch to pick and stow items in fulfillment centers, letting it handle flexible fabric storage pods and unpredictable product shapes. Amazon says the robot’s tactile systems allow it to respond to pressure, contact and motion in real time to carry out tasks that typically call for human dexterity. Cash App Debuts Payment Links to Make P2P Less Formal Ahold Delhaize Sees US Online Grocery Sales Jump 22% Shopify Expands Grip on Checkout as AI-Driven Shopping Surges Amazon Accelerates Medical Ambitions With Faster Prescription Deliveries and AI Insights Get PYMNTS Today, AI, B2B and more. We’re always on the lookout for opportunities to partner with innovators and disruptors.
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| Robots con forma de conejo: la nueva estrategia para controlar … | https://www.elimparcial.com/locurioso/2… | 0 | Mar 08, 2026 08:00 | active | |
Robots con forma de conejo: la nueva estrategia para controlar pitones en FloridaDescription: Autoridades del sur de Florida comenzaron a usar conejos robóticos con energía solar para detectar y retirar pitones birmanas en los Everglades. Content: |
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| Humanoid robotics making big strides | http://www.ecns.cn/china/2026-03-03/det… | 1 | Mar 08, 2026 00:02 | active | |
Humanoid robotics making big stridesURL: http://www.ecns.cn/china/2026-03-03/detail-ihfaizcc2495140.shtml Content:
China has unveiled a framework to standardize its rapidly expanding humanoid robotics industry, as policymakers, companies and researchers seek to address growing technical fragmentation in the sector. The 2026 edition of the humanoid robots and embodied intelligence standard system was released on Saturday at the annual meeting of the humanoid robots and embodied intelligence standardization technical committee in Beijing's E-Town. Organizers described it as the country's first top-level design covering the entire industry chain and life-cycle of humanoid robotics and embodied intelligence. The framework comes at a pivotal moment. According to figures disclosed by Ministry of Industry and Information Technology in January, the country has more than 140 humanoid robot manufacturers and over 330 product models. Executives widely refer to 2026 as a transitional year for mass production in the sector. Public interest has also accelerated. E-commerce giant JD reported a surge in robotics-related sales following humanoid robots' recent high-profile Spring Festival performances, underscoring rising consumer visibility, said Zheng Xiaodan, head of embodied intelligent robotics business at JD. Yet scaling remains complex. In roundtable discussions, company executives pointed to manufacturing consistency as a key challenge. "Humanoid robots involve long supply chains stretching from supply networks and components to complete systems, operating systems and algorithms," said Chen Jianyu, founder of Robotera. Gao Jiyang, founder of Galaxea, added that even subtle mechanical differences between units can be amplified when integrated with large foundation models, requiring systematic calibration to align sensors, structures and software within a unified framework. Hardware maturity remains uneven. Participants noted that key components such as high-torque joints and dexterous hands have yet to achieve stable economies of scale, keeping costs high and limiting predictable expansion. Beyond hardware, data was repeatedly described as a structural bottleneck. "We are still lacking high-quality embodied data and relevant standards," said Wang Zhongyuan, director of the Beijing Academy of Artificial Intelligence, adding that inconsistent data formats and labeling methods across firms have created isolated silos, forcing developers to duplicate work. The newly issued framework seeks to address these challenges, structured across six areas â foundational standards, brain-inspired computing and intelligent processing, body structures and components, complete systems, applications, alongside safety and ethics. It reflects coordinated input from government agencies, research institutes, enterprises and universities. "To enable robots to truly work in real-world scenarios, industry-wide standards are indispensable," said Wang Xingxing, founder of Unitree Robotics and a committee vice-chair. He identified unified task definitions, evaluation systems and safety standards as immediate priorities. Globally, no dominant humanoid robotics standards have yet emerged. Xu Jincheng, founder and CEO of tactile-sensing firm PaXini Tech, said China's achievements in embodied intelligence have drawn global attention, and that continued technological progress may position the country to play a significant role in shaping future international standards. EconoScope | China leads in humanoid robot sports, powers robotics innovation Chinese humanoid robotics company achieves the world's first multi-robot collaborative training
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