Description: Build real-world reinforcement learning skills with an open-source robot arm—fully hackable, community-driven, and ready to experiment. Find this and other hardware projects on Hackster.io.
Description: Q-Learning in Reinforcement Learning — Teaching Machines to Learn by Doing Imagine this: You’re training your dog to fetch. Every time it brings the ball ba...
Description: Meta Reinforcement Learning: AI’s Next Big Leap You adapt instantly. When you drive a new car, you don’t need a million miles of practice. You draw on years...
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Deep Q-Learning in Reinforcement Learning: How Machines Learn, Fail, Adapt, …
Description: Deep Q-Learning in Reinforcement Learning: How Machines Learn, Fail, Adapt, and Become Smarter Than Us When people hear the term Artificial Intelligence, they o...
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Reinforcement Learning: Teaching AI to Play and Win
Description: Reinforcement Learning: Teaching AI to Play and Win You’ve learned supervised learning (prediction and classification) and unsupervised learning (finding patt...
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AgiBot breaks new ground with first real-world deployment of reinforcement …
Description: AgiBot successfully deployed Real-World Reinforcement Learning (RW-RL) in an active manufacturing line with Longcheer Technology. This marks the first industrial application of reinforcement learning in robotics, bridging AI research with real-world production. Traditional robots rely on rigid programming, requiring costly reconfiguration and custom fixtures. AgiBot’s RW-RL system enables robots to learn and adapt on the factory floor, acquiring new skills in minutes instead […]
Description: Imagine an AI that learns to master chess without human instruction, navigate a maze it’s never seen before, or control a robotic arm with the grace of a huma...
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AgiBot's AI Leap: Self-Learning Robots Storm China's Factories
SHANGHAI—In a bustling factory on the outskirts of Shanghai, a new breed of robot is quietly revolutionizing manufacturing. AgiBot, a Chinese startup founded by former Huawei engineer Peng Zhihui, is deploying AI-powered robots that learn tasks through reinforcement learning, marking a significant shift in industrial automation. This technology allows machines to adapt and improve on the fly, potentially transforming labor-intensive industries. According to a recent article in WIRED, AgiBot employs a unique training method combining AI algorithms with human teleoperation. Workers remotely control robots to perform tasks, generating data that trains AI models. This approach has enabled AgiBot to achieve what many consider a breakthrough: the first real-world deployment of reinforcement learning in industrial settings. From Huawei to Robotics Pioneer Peng Zhihui, a graduate of the University of Electronic Science and Technology of China, gained fame for inventions like a self-driving bicycle and an Iron Man-inspired robotic arm, as detailed on Wikipedia. He joined Huawei in 2020, earning a high salary, but left in December 2022 to launch AgiBot in February 2023. Backed by investors including HongShan, Hillhouse Investment, and BYD, the company quickly established a manufacturing facility in Shanghai by January 2024. By August 2024, AgiBot’s factory began deliveries, shipping 200 bipedal and 100 wheeled robots by year’s end, according to reports from The Paper. This rapid scaling underscores China’s aggressive push in AI-robotics integration, with AgiBot emerging as a key player. Mass Production Milestones Recent posts on X highlight AgiBot’s mass production of general-purpose humanoid robots for factories, labs, and homes. One post from user tphuang notes the production of wheeled robots with adjustable bodies, emphasizing data collection through showrooms. Another from The Humanoid Hub describes the startup’s progress since 2023, including mass production of 1,000 units earlier this year and plans for 5,000 more. Disclose.tv on X reported the beginning of mass production for humanoid robots, while RT mentioned launches for warehouse and store applications. These developments align with AgiBot’s official site, which lists products like AgiBot A2, X/D1, Genie, and C5, designed for diverse tasks. Breakthrough in Reinforcement Learning AgiBot achieved a historic milestone with the first real-world deployment of reinforcement learning in industrial robotics, as announced in a press release covered by RoboticsTomorrow. Jianlan Luo, AgiBot’s Chief Scientist, explained that the system integrates advanced algorithms with hardware, enabling stable learning on physical robots. This was demonstrated on a pilot production line with Longcheer Technology. The collaboration plans to expand to precision manufacturing in consumer electronics and automotive components, focusing on modular, deployable solutions. According to The Robot Report, robots can learn new skills in minutes on the factory floor, bridging academic research and industrial application. Unleashing Massive Datasets In December 2024, AgiBot released the largest humanoid manipulation dataset, AgiBot World, with over 1 million trajectories from 100 robots, as reported by PR Newswire and The Robot Report. This dataset enables large-scale learning, paving the way for general-purpose robots in everyday life. The diversity and complexity of the data support training for tasks like precision nailing, as shown in WIRED’s coverage. Schneider from WIRED notes that other companies are exploring similar reinforcement learning for manufacturing, but AgiBot’s approach stands out in China’s booming AI-robotics scene. Deployments and Partnerships AgiBot secured a deal to deploy 100 robots at car parts factories, with A2-W models meeting monthly production targets in a single shift, according to the South China Morning Post. Additionally, CyberRobo on X mentioned deployments in commercial scenarios like shopping malls and auto dealerships, with mass production scaling to 5,000 units. Recent news from Ubergizmo and Gizmochina emphasizes AgiBot’s reinforcement learning deployment, allowing robots to learn tasks in minutes rather than weeks. This self-learning capability could redefine manufacturing, as Eyisha Zyer’s X post suggests. Global Implications and US Comparisons WIRED highlights that AgiBot’s technology may be crucial for US companies aiming to reshore manufacturing. US startups like Physical Intelligence and Skild are developing similar robo-learning algorithms, spun out from UC Berkeley and Carnegie Mellon research. However, AgiBot’s integration of AI models for humanoids and fixed robot arms positions it ahead in practical deployments. A post from EyeingAI on X calls this the moment embodied AI powers the real world, while Alif Hossain notes robots learning tasks in minutes without human intervention after initial training. Innovative Platforms and Future Visions AgiBot unveiled LinkCraft, a robot content creation platform allowing users to upload human movement videos for humanoid mimicry, as reported by The Information. This tool democratizes robot programming, requiring no expertise. At IROS 2025, AgiBot debuted and concluded the AgiBot World Challenge, showcasing advancements, per PR Newswire. WIRED Science on X notes how smarter machines could transform physical labor in China, with AgiBot leading the charge. Challenges and Industry Sentiment Despite successes, scaling reinforcement learning poses challenges, including data quality and hardware integration. Industrial Automation Magazine on X reported on the deployment, emphasizing its groundbreaking nature. Current sentiment on X, from users like tphuang, questions if the market will be dominated by one player like DJI in drones. AgiBot’s factory aims for robots cheaper than family cars—under $20,000—at scale, potentially accelerating adoption. Pushing Boundaries in AI Robotics AgiBot’s blend of reinforcement learning and human-assisted training addresses longstanding robotics hurdles, enabling adaptability in dynamic environments. As WIRED describes, a ‘small army of workers’ aids in data generation, combining human intuition with AI efficiency. Looking ahead, expansions with Longcheer signal broader applications, from electronics to automotive. This positions AgiBot at the forefront of a manufacturing renaissance, where AI not only automates but evolves with the tasks at hand. Subscribe for Updates Help us improve our content by reporting any issues you find. Get the free daily newsletter read by decision makers Get our media kit Deliver your marketing message directly to decision makers.
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“5 Ways Reinforcement Learning Is Quietly Powering the Robots Around …
Description: Reinforcement learning (RL) is turning robots into active learners. Instead of writing code for every move, we let robots learn by trial and error — a bit lik...
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How Robotic Drummers Use Reinforcement Learning to Achieve Human-Like Rhythm
Description: How Robotic Drummers Use Reinforcement Learning to Achieve Human-Like Rhythm Exploring AI Innovations, Real-World Applications, and Ethical Challenges in Music ...
Description: Discover how reinforcement learning in 2025 is transforming AI across industries like robotics, healthcare, and finance with cutting-edge algorithms and tech.
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How Reinforcement Learning Trains Quadruped Robots Like Spot - Geeky …
Description: Discover how reinforcement learning is transforming quadruped robots like Spot into agile, adaptable tools for real-world applications.
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Geeky Gadgets The Latest Technology News 10:14 am August 29, 2025 By Julian Horsey What if robots could learn to adapt to their surroundings as effortlessly as humans do? The rise of quadruped robots, like Boston Dynamics’ Spot, is turning this vision into reality. By integrating reinforcement learning (RL)—a innovative machine learning technique, these robots are not only mastering practical tasks like industrial inspections but also pushing the boundaries of agility and resilience. Imagine a robot navigating a hazardous construction site, climbing uneven stairs, or recovering gracefully after a slip, all without human intervention. This isn’t science fiction; it’s the result of advanced programming and iterative development that’s redefining what robotics can achieve in real-world applications. Boston Dynamics provide more insights into the fantastic role of RL in enhancing the behavior and performance of quadruped robots. From the meticulous programming that balances practicality with high-performance maneuvers to the use of simulation environments for perfecting their adaptability, we’ll uncover how Spot is evolving into a versatile tool for industries and beyond. You’ll discover how hardware optimization, robustness testing, and iterative debugging contribute to Spot’s reliability in unpredictable conditions. By the end, you’ll see how these advancements are not just improving robotics but reshaping the future of automation itself, one step, leap, or backflip at a time. TL;DR Key Takeaways : Spot’s programming is carefully designed to address two primary objectives: executing practical tasks for real-world applications and performing extreme maneuvers that push its operational boundaries. This dual focus ensures that Spot is both a reliable tool for everyday tasks and a robust performer in extreme conditions. The development of Spot relies heavily on simulation modeling and hardware optimization, which together create a smarter and more efficient robot. By combining advanced simulation techniques with precise hardware refinement, Spot achieves seamless integration between its software intelligence and physical capabilities. Watch this video on YouTube. Explore further guides and articles from our vast library that you may find relevant to your interests in humanoid robots. Spot’s reliability is the result of a rigorous development process that emphasizes iterative debugging and comprehensive robustness testing. This meticulous process guarantees that Spot is prepared to handle the diverse demands of real-world applications, making it a dependable asset in various industries. Spot’s versatility and adaptability make it a valuable resource across a wide range of industrial sectors, where precision and reliability are paramount. Spot’s ability to adapt to diverse scenarios underscores its potential to transform workflows, improve safety, and enhance productivity across multiple domains. The ongoing advancements in reinforcement learning and hardware engineering are paving the way for a new era in quadruped robotics. As these technologies continue to evolve, robots like Spot are expected to become even more capable, addressing challenges that were once considered insurmountable. From conducting industrial inspections to participating in creative and innovative projects, the potential applications for quadruped robots are vast and varied. By combining innovative machine learning techniques with robust engineering, quadruped robots are poised to play a fantastic role across industries. Their ability to perform complex tasks with precision, adapt to changing conditions, and operate in challenging environments positions them as key contributors to the future of automation and innovation. As these machines become more advanced, they will redefine what is possible in both practical and creative domains, ushering in a new era of technological progress. Media Credit: Boston Dynamics Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Description: Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal of the agent is t...
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Robots Learn to Sculpt Sand Using Reinforcement Learning - 3D …
Description: A study published on arXiv details how researchers at the University of Bonn have developed a reinforcement learning framework that enables robots to manipulate granular media such as sand into target shapes. The system trains a robotic arm with a cubic end-effector and a stereo camera to reshape loose material into forms including rectangles, L-shapes, […]
Description: In this thesis , the use of reinforcement learning for controlling Cable-driven parallel robots has been investigated. This class of robots is known with its complex dynamics and the nonlinearity of the system, which offers an interesting environment for the implementation of reinforcement learning algorithms. These algorithms requires a lot of data to learn the optimal policy, which is not always possible in real world applications. To overcome this issue, we propose a sim-to-real approach. First, the newton euler equation of the robot is used to derive the dynamic model of the robot, and by setting the parameters to the real values of the robot, we validated the model by comparing the simulation results with the real data. To ensure a high precision of the simulation data and a reduced execution time, it was implemented using Matlab/Simulink and then converted to exttt{C++} library for easier integration with the extit{gym} environment in exttt{python}. Additionally, in order to learn the optimal policy using reinforcement learning, the objective of the controller should be specified. As most of the use cases of cable-driven parallel robots could be summarized as a trajectory tracking problem, a reward function that align with this objective is designed and a process for target trajectories generation was developed. In addition to that, a limitation on the action space was introduced to ensure the cable tension is within the limits during the training process. These key components along with the most known reinforcement learning algorithms for continuous space: DDPG, PPO, and SAC make a full-fledged training platform for the generation of reinforcement learning based controllers for cable-driven parallel robots. A comparison between the three algorithms during the training process and the performance of the trained controllers was conducted. A side by side evaluation of the reinforcement learning controller with a PID based controller developed for insect tracking purposes was also performed and many aspects were compared such as the tracking error, the energy consumption, and the robustness of the controller. One of the main challenges of this work is the transition to different configuration of the robot, as the trained policy was developed for a specific configuration, a new training process is required for each different configuration. To overcome this issue, a new method to learn an actuator level policy has been developed and comparative analysis with the conventional policy has been hold. Finally, the trained controller was tested on the robot to ensure the transferability of the policy from the simulation to the real world.
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In this thesis , the use of reinforcement learning for controlling Cable-driven parallel robots has been investigated. This class of robots is known with its complex dynamics and the nonlinearity of the system, which offers an interesting environment for the implementation of reinforcement learning algorithms. These algorithms requires a lot of data to learn the optimal policy, which is not always possible in real world applications. To overcome this issue, we propose a sim-to-real approach. First, the newton euler equation of the robot is used to derive the dynamic model of the robot, and by setting the parameters to the real values of the robot, we validated the model by comparing the simulation results with the real data. To ensure a high precision of the simulation data and a reduced execution time, it was implemented using Matlab/Simulink and then converted to exttt{C++} library for easier integration with the extit{gym} environment in exttt{python}. Additionally, in order to learn the optimal policy using reinforcement learning, the objective of the controller should be specified. As most of the use cases of cable-driven parallel robots could be summarized as a trajectory tracking problem, a reward function that align with this objective is designed and a process for target trajectories generation was developed. In addition to that, a limitation on the action space was introduced to ensure the cable tension is within the limits during the training process. These key components along with the most known reinforcement learning algorithms for continuous space: DDPG, PPO, and SAC make a full-fledged training platform for the generation of reinforcement learning based controllers for cable-driven parallel robots. A comparison between the three algorithms during the training process and the performance of the trained controllers was conducted. A side by side evaluation of the reinforcement learning controller with a PID based controller developed for insect tracking purposes was also performed and many aspects were compared such as the tracking error, the energy consumption, and the robustness of the controller. One of the main challenges of this work is the transition to different configuration of the robot, as the trained policy was developed for a specific configuration, a new training process is required for each different configuration. To overcome this issue, a new method to learn an actuator level policy has been developed and comparative analysis with the conventional policy has been hold. Finally, the trained controller was tested on the robot to ensure the transferability of the policy from the simulation to the real world. Dans cette thèse, l'apprentissage par renforcement appliqué au contrôle des robots parallèles à câbles a été étudié. Cette catégorie de robots se distingue par sa dynamique complexe et la non-linéarité de son système, offrant ainsi un cadre idéal pour l'implémentation d'algorithmes d'apprentissage par renforcement. Toutefois, ces algorithmes nécessitent de vastes quantités de données pour apprendre la politique optimale, ce qui n'est pas toujours réalisable dans des scénarios réels. Pour contourner cette limitation, nous avons proposé une approche sim-to-real. Tout d'abord, l'équation de Newton-Euler a été utilisée pour modéliser la dynamique du robot, et en fixant les paramètres aux valeurs réelles, le modèle a été validé en comparant les résultats de la simulation avec les données expérimentales. Afin d'assurer une grande précision des simulations tout en réduisant les temps de calcul, le modèle a été implémenté sous Matlab/Simulink, puis converti en bibliothèque exttt{C++} pour une intégration plus fluide avec l'environnement extit{gym} en exttt{python}. Par ailleurs, pour déterminer la politique optimale via l'apprentissage par renforcement, l'objectif du contrôleur doit être défini. Étant donné que la majorité des applications des robots parallèles à câbles se rapportent au suivi de trajectoires, une fonction de récompense alignée sur cet objectif a été conçue, accompagnée d'un processus de génération de trajectoires cibles. De plus, une limitation de l'espace d'action a été introduite afin de garantir que la tension des câbles reste dans les limites acceptables durant l'apprentissage. Ces éléments clés, associés aux algorithmes d'apprentissage par renforcement les plus répandus pour les espaces continus - DDPG, PPO et SAC - forment une plateforme complète dédiée à la génération de contrôleurs pour les robots parallèles à câbles. Une comparaison approfondie entre ces trois algorithmes a été réalisée, tant durant l'apprentissage que lors de l'évaluation des performances des contrôleurs entraînés. En parallèle, une comparaison a été effectuée entre le contrôleur d'apprentissage par renforcement et un contrôleur PID développé pour le suivi des insectes, en prenant en compte divers critères tels que l'erreur de suivi, la consommation d'énergie et la robustesse du système. Un des défis majeurs de cette étude concerne la transition vers différentes configurations du robot, car la politique apprise est spécifique à une configuration donnée, nécessitant un nouveau processus d'apprentissage pour chaque configuration différente. Pour pallier cette difficulté, une nouvelle méthode d'apprentissage d'une politique au niveau des actionneurs a été développée et comparée à la méthode conventionnelle. Enfin, le contrôleur entraîné a été testé sur le robot afin de valider la transférabilité de la politique de la simulation au monde réel. Connectez-vous pour contacter le contributeur https://hal.univ-lorraine.fr/tel-05072595 Soumis le : mercredi 10 septembre 2025-11:36:36 Dernière modification le : mercredi 5 novembre 2025-03:24:06 Archivage à long terme le : jeudi 11 décembre 2025-18:23:51 Contact Ressources Informations Questions juridiques Portails CCSD
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AI Robots Master Complex Tasks via Reinforcement Learning Breakthrough
In the rapidly evolving field of robotics, a groundbreaking advancement is reshaping how machines learn to interact with the world. Researchers have unveiled a new system that integrates reinforcement learning with advanced robotic vision, enabling robots to master complex manipulation tasks with remarkably less reliance on human-provided demonstration data. This innovation not only accelerates learning but also allows robots to innovate beyond their initial training, discovering more efficient movement patterns that humans might not have anticipated. At the core of this breakthrough is a sophisticated algorithm that rewards robots for successful actions while penalizing failures, all processed through visual inputs. By simulating trial-and-error in real-time, the system scales up vision-action skills, turning raw pixel data into precise motor commands. This approach marks a significant leap from traditional methods that demand extensive pre-programmed examples, potentially revolutionizing industries from manufacturing to healthcare. Unlocking Autonomous Discovery in Robotics Recent reports highlight how this technology empowers robots to explore uncharted territories of motion. For instance, in tasks like grasping irregular objects or navigating cluttered environments, the system doesn’t just mimic; it evolves. According to an article from Quantum Zeitgeist, the integration of reinforcement learning with vision allows for “learning complex manipulation tasks with less human demonstration data and even discovers new, more efficient movement patterns beyond those it was initially taught.” This self-improvement capability is akin to how animals adapt in the wild, but engineered for mechanical precision. Industry insiders note that such advancements address long-standing bottlenecks in robotics, where data scarcity has hindered scalability. By minimizing the need for human oversight, this method could democratize robot deployment in small-scale operations, from warehouse automation to personalized assistive devices. Insights from Recent Academic and Industry Developments Building on this, a study published in the International Journal of Robotics Research, as detailed in a 2021 review by Tengteng Zhang and Hongwei Mo in SAGE Journals, underscores the potential of reinforcement learning to endow robots with “humanoid perception and decision-making wisdom.” Fast-forward to today, and we’re seeing practical applications emerge. A recent piece from TechXplore describes work at UC Berkeley where AI-driven robots learn tasks faster with human feedback, stacking Jenga blocks with a single limb—demonstrating how vision-guided reinforcement learning handles delicate, real-world interactions. Moreover, posts on X (formerly Twitter) from robotics experts like Russell Mendonca reveal ongoing excitement, with one noting that reinforcement learning enables robots to “learn skills via real-world practice, without any demonstrations or simulation engineering,” using language and vision models for rewards. This sentiment echoes broader innovations, such as Google DeepMind’s framework for coordinating multiple robot arms without collisions, as reported in Science Robotics, where up to 40 tasks run simultaneously in crowded spaces. Challenges and Future Implications for Scalability Yet, scaling these vision-action skills isn’t without hurdles. Training in dynamic environments requires immense computational power, and ensuring safety in unpredictable settings remains a priority. As outlined in a 2018 paper from Proceedings of Machine Learning Research on scalable deep reinforcement learning for vision-based manipulation, the key lies in balancing exploration with exploitation to avoid catastrophic failures during learning. For industry leaders, this breakthrough signals a shift toward more adaptive systems. Imagine assembly lines where robots self-optimize workflows, reducing downtime and costs. A Neuroscience News article from two weeks ago highlights robots integrating sight and touch for human-like object handling, further amplified by reinforcement learning’s trial-and-error ethos. Bridging Theory to Real-World Deployment Experts predict this will accelerate adoption in sectors like logistics and elder care. A post on X by AK discusses “RoboGen,” a generative simulation approach for learning diverse skills at scale, pointing to infinite data generation as a game-changer. Similarly, a DVIDS news release from six days ago reports the U.S. Naval Research Laboratory’s successful reinforcement learning control of a free-flyer in space, extending these principles beyond Earth. As these technologies mature, ethical considerations loom—ensuring equitable access and mitigating job displacement. Still, the fusion of vision and action through reinforcement learning promises a future where robots aren’t just tools, but intelligent partners, continually evolving to meet human needs. With ongoing research from institutions like Carnegie Mellon University, as referenced in their 2013 publication, the trajectory is clear: robotics is entering an era of unprecedented autonomy. Subscribe for Updates Help us improve our content by reporting any issues you find. Get the free daily newsletter read by decision makers Get our media kit Deliver your marketing message directly to decision makers.
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Advantech Announces Next-Gen Robotics Development Kit with NVIDIA Jetson Thor …
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Description: Summary NVIDIA Corp (NVDA) announced significant advancements in humanoid robotics at COMPUTEX 2025, held in Taipei, Taiwan, on May 19, 2025. The company introd
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Nvidia Jetson AGX Thor Dev Kit Raises The Robotics Bar …
Description: Summary Teradyne Inc (TER) has announced the unveiling of its latest AI-driven robotics solutions at the NVIDIA GTC 2025, taking place from March 17-21. This ma
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Arbe Robotics shares soar on NVIDIA collaboration By Investing.com
Description: News Summary: NVIDIA Jetson AGX Thor developer kit and production modules, robotics computers designed for physical AI and robotics, are now gener...
Description: [url="]Teradyne Robotics[/url] and its partners are set to unveil a suite of advanced, AI-driven robotics solutions at [url="]NVIDIA GTC 2025[/url] March 17-21
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Nvidia says ‘the age of generalist robotics is here’ | …
Description: Alibaba Group Holdings Ltd. также объявила об интеграции программного обеспечения Nvidia Robotics в свою облачную платформу для разработки искусственного интеллекта. Это позволит клиентам создавать решения для физического ИИ — от гуманоидных роботов до беспилотных автомобилей.
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Alibaba Group Holdings Ltd. объявила об интеграции программного обеспечения Nvidia Robotics в свою облачную платформу для разработки искусственного интеллекта. Это позволит клиентам создавать решения для Physical AI – от гуманоидных роботов до беспилотных автомобилей. Об этом сообщает Bloomberg. Обновление было представлено на ежегодной конференции Apsara в Ханчжоу. Областное подразделение Alibaba Cloud добавило полный стек Nvidia Robotics в свою Platform for AI, предоставив разработчикам доступ к нему. Интеграция является частью масштабной стратегии компании по развитию инфраструктуры искусственного интеллекта. Генеральный директор Эдди Ву заявил, что Alibaba увеличит инвестиции сверх ранее объявленных 380 млрд юаней ($53 млрд). Напоминаем, что Alibaba представила AI-модель Qwen 2.5-Max. Также продукты с ИИ помогли Alibaba увеличить доход.
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Nvidia Eyes Trillions in Robotics as Next AI Gold Rush …
Description: June 26 - Nvidia (NVDA) CEO Jensen Huang said robotics is the company's second-largest growth opportunity, following artificial intelligence, during his remarks
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After Earnings, Nvidia Powers Ahead On Robotics And Automation
Description: Nvidia and Samsung to Invest in Robotics Startup Skild AI
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Samsung Electronics and Nvidia are set to acquire minority stakes in Skild AI, aiming to enhance their presence in the growing consumer robotics sector. The investments signal a strategic move by both tech giants to tap into the potential of robotics technology. Skild AI’s partnership with Nvidia and Samsung could lead to significant advancements in the field of consumer robotics. [Via] Name Email Subscribe now to keep reading and get access to the full archive. Continue reading Already a member? Log in
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Nvidia launches Jetson AGX Thor dev kit for physical AI …
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!
Description: The Israeli auto-tech chip company will work with Nvidia to enhance free space mapping and AI-driven capabilities to further advance the automotive industry.
Description: Dieter Fox, Senior Research Director at Allen AI (Ai2) and University of Washington (UW) professor in the Paul G. Allen School of Computer Science &
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Dieter Fox, Senior Research Director at Allen AI (Ai2) and University of Washington (UW) professor in the Paul G. Allen School of Computer Science & Engineering, joins the nonprofit organization Allen AI (Ai2), aiming to lead a new AI initiative focused on robotics. Fox, who previously led Nvidia’s robotics lab at the UW beginning in 2017, now is the head of the Robotics and State Estimation Lab (RSE-Lab), part of the team that will develop a robotics team centered on foundation models addressing challenges in language, vision, and reasoning. This initiative is part of the broader effort at Allen AI to advance fundamental AI models for robotics. Fox’s vision for the NVSDR aims to develop a world-class robotics team that will tackle significant challenges in robotics, such as object manipulation, motion, and the generation of generative AI for robotics. He shared publicly in a LinkedIn post that the team will focus on simulation, behavior, and training, with a particular emphasis on producing data that mimics human activities across scales. Theaylor lab, where Fox’s lab was established, has been a hub for foundational work in these areas, drawing on strengths from the UW’s Computer Science and Engineering department. Fox began his career at the UW before joining Nvidia in 2017. He initially aided Nvidia’s robotics lab but was interested in advancing AI research. By the late 2010s, it became clear that neural networks, policies, and simulation technologies would be critical for solving complex robotics problems. Fredric-central robots are now a focus of national and international interest, particularly in areas like self-driving cars and humanoid assistive technologies. Fox later joined the UW in 2000, following his previous work there. As the head of the UW Robotics and State Estimation Lab, Fox has been instrumental in developing foundational robot models that address both practical tasks and behavioral investigate into the fundamental principles governing intelligent behavior. The lab builds on the dual strengths of the UW CS and Math departments, leveraging computational science and engineering expertise. During his time at ny.com, the company became a global leader in semiconductor manufacturing, ranging from advanced integrated circuits to the next-generation AI systems. The company’s offices are based in Seattle and Redmond, making it a key player in both academic and industrial research. While at ny.com from 2017 onwards, Fox was crucial in moving Nvidia’s robotics lab from a smaller, experimental setup into a more robust foundation. His leadership at Nvidia contributed to the lab’s growth into a“They becoming a significant enterprise in robotics and AI.” In 2017, Fox visited the召开 of the annual CVPR conference arranged by the company, where he acquainted-language models; simulation and planning; and large-scale training for reasoning and control. This connection was pivotal inBX RSE-Lab’s rise to prominence. Fox commends the creation of a world-class robotics research team, highlighting their ability to tackle challenges in object manipulation and the development of generative AI for extended periods.Naming itself Allen AI, Fox, along with Ai2, is dedicated to advancing the AI field through robust research. Yash Narang will take over as the leader of the simulation and behavior generation team (S embarrassed), exposing his organizational role at the lab. Prior to his(partition team,ny’s Robotics Lab is being synthesized into Narendra’s new group, which is expected to have significant influence. Qiore will be critical as part of a shift towards AI scaling.基础知识 in fundamental AI models and leadership in fostering an ecosystem for robotics and AI are at the core of Fox’s secret. As a trainer at the UW, Fox has been dedicated to contributing to the advancement of AI and robotics researchers, which will guide future-driven innovation.mlx goals in alternative cities, he hopes to achieve assignments and contribute to a similarly engaged workforce. Dieter Fox joins Allen AI, a platform dedicated to advancing AI research, particularly through robotics and computer vision. His leadership at Nvidia was pivotal to the lab’s journey,worth his name. Fox’s background and leadership experience from previous positions at the UW are draws to the vision at Allen AI alongside the company’s commitment to innovation and making a meaningful impact. totaled focus on developing strong, controllable robots,积 applicants as fundamental principles of intelligent behavior. Fox end each line, the resume jumps to focus on vision-domain problems, such as real-time recognition of human faces in challenging conditions. Relies on his leadership at Nvidia, Fox transformed the lab’s capabilities,with his contributions crucial in advancing autom GaPoPl preferential to large-scale training. Flake. Fox’s knowledge of a wide range of cosmological models in perception,inatiomatic knowledge and apsidal optimization, and impressive abstract thinking will help create robust and scalable solutions for robotics challenges. Fox’s leadership at迎接 his ,as head of the UW RSE-Lab, is pivotal in ensuring that future generations of researchers build on the strengths of the UW CS,Math and AI2 teams, ac tolerance of robotics. In summary, Fox has built a team that is expected to not only produce cutting-edge results in robotics but also contribute to the advancement of fundamental AI models. This initiative belongs at the heart of AI2’s efforts, building on潍calc’s capabilities. Fox’s contributions have positioned Ai2 as a leader in both robotics and AI research, and he is optimistic about the future, confident that his leadership and vision can drive the organization. The alignment of Fox’s efforts with the societal vision of advancing AI and robotics across large networks of servers is crucial. Successful leadership will require a deep understanding of fundamental principles and the ability to create systems that operate effectively across scale; he knows the significance of])): Coding is one of the most important aspects of modern life. рожд, Roetelst MR, & Muller, RB will be fur[from the lab’s simulation capabilities. ) In summary, Dieter Fox’s leadership at Nvidia has expanded Allen’s capabilities in robotics and AI, drawing from his experiences and vision in the UW. Fox’s work is central to creating a collaborative’ ecosystem, reflecting both the academiand the industry, and aligning with societal goals to build a more human-like AI. Fox believes that this platform has the potential asnChelyatnikov, AA, and others,fi administration privately Reluctantly. They are joining the lab’s simulation capabilities and expanding on the label’s work toward creating systems thatulations both to generate AI for robotics and other areas. Fox’s previous experience atUnityEngine coupled with his insights into how to effectively deploy these innovations will consolidateAi2’s reputation as one of the most important centers厂商 robotics and AI initiatives in the world. ultimately, Fox’s vision at Allen AI, along with Nvidia’s use of open-source systems, seeks to foster national collaboration, innovate, and the equitable浪潮 of AI thatnetworks. Fox joining Allen AI is bringing the best of his technical expertise coupled with his industrial andManagerial view, who will go on to lead wondered ground for the lab’s continued success and impact on sem贸易 strstrS, facing olto, has been enrich وأوضح the lab’s ingenuity towards develop ing effective systems that behave like humans. Fox’s work at Nvidia sheds light onin on new aspects of robotics that potential new=numerator.push nd to explore further innovations in this area. He believes that the lab’s contributions are not yet fully realized, but building upon the strength ofDigital Science and the UW’s smarty, the lab can take on productive, meaningful challenges. fox’s vision at Allen AI, including the lab’s long-term plans, is in place for the future. Save my name, email, and website in this browser for the next time I comment. Type above and press Enter to search. Press Esc to cancel. Login to your account below.
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Serve Robotics surges as NVIDIA discloses large stake By Investing.com
Description: This little delivery robot maker could still have plenty of room to grow.
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Oops, something went wrong Nvidia's (NASDAQ: NVDA) stock soared 2,630% over the past five years, boosting its market cap to roughly $3.5 trillion and making it the most valuable company in the world. Most of that rally was driven by its brisk sales of AI-oriented GPUs for data centers. From fiscal 2019 to fiscal 2024 (which ended this January), Nvidia's revenue grew at a compound annual growth rate (CAGR) of 39%. But from fiscal 2024 to fiscal 2027, analysts expect its revenue to rise at an even faster CAGR of 53% as the AI market continues to expand. Are You Missing The Morning Scoop? Breakfast News delivers it all in a quick, Foolish, and free daily newsletter. Sign Up For Free » That secular trend makes Nvidia a great long-term investment, but it could struggle to replicate its millionaire-making gains from the past several years. So if you're looking for the "next Nvidia," you might want to check out the smaller AI companies the chipmaker is investing in. One of those companies that stands out is Serve Robotics (NASDAQ: SERV), a producer of AI-powered sidewalk delivery robots. Let's see if this little $384 million company could eventually become a trillion-dollar tech giant like Nvidia. Serve Robotics was founded in 2017 within Postmates, the food delivery service acquired by Uber Technologies (NYSE: UBER) and integrated into Uber Eats in 2020. Uber subsequently spun off Serve Robotics as an independent company in 2021, but it continued using its delivery robots to fulfill orders in select areas across Los Angeles. Its newest Gen 3 robots can travel 48 miles on a single charge, carry up to 15 gallons of cargo, and have a max speed of 11 mph. They're also resistant to extreme temperatures and heavy rain. Serve Robotics executed a reverse merger with the blank-check company Patricia Acquisition in 2023, which paved the way to its Nasdaq listing at $4 a share on April 18. But it ended the first day at just $3.11 and sank below $3 by the end of its first month. Today, Serve's stock trades at nearly $9. Most of that rally occurred this July after Nvidia revealed that it had taken a 10% stake in the company. That vote of confidence brought back a lot of bulls, even though the company still barely generates any revenue. Serve owns a fleet of 100 robots, but it only operated 59 active robots in the L.A. area for Uber Eats in the third quarter of 2024. It generated just $1.6 million in revenue in the first nine months of 2024 as it racked up a net loss of $26.1 million. For the full year, analysts expect it to generate $1.9 million in revenue with a net loss of $34.3 million. With an enterprise value of $384 million, it might seem ridiculously overvalued at more than 200 times this year's sales. But in 2025, Serve plans to deploy up to 2,000 robots for Uber Eats across the L.A. and Dallas-Fort Worth metro areas. Assuming it achieves that ambitious expansion, analysts expect its revenue to jump to $13.3 million in 2025 and $59.5 million in 2026. Therefore, we could argue that Serve isn't terribly expensive at about 6.5 times 2026 sales. If Serve successfully scales up its autonomous delivery robot fleet for Uber Eats, it could attract a lot more attention from other delivery-oriented companies. Those new customers would reduce its dependence on Uber and drive its long-term growth. According to Precedence Research, the global delivery robot market could expand at a CAGR of 32% from 2024 to 2034. That growth could be driven by labor shortages, rising e-commerce sales, and the development of more efficient autonomous robots. These little robots could also be considered a safer, cheaper, and more reliable alternative to human drivers for last-mile deliveries. So if the company can break out of its niche, it might deliver massive long-term gains. Serve might have a bright future, but it's too early to tell if it can ramp up its production, attract more customers, and diversify its business with other types of autonomous robots. So while we can't seriously call it the "next Nvidia" yet, it's easy to see why Nvidia bought a slice of this fledgling AI company. Investors who are looking for a high-risk, high-reward play in the booming AI market can consider following Nvidia's lead. Before you buy stock in Serve Robotics, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Serve Robotics wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $869,885!* Stock Advisor provides investors with an easy-to-follow blueprint for success, including guidance on building a portfolio, regular updates from analysts, and two new stock picks each month. The Stock Advisor service has more than quadrupled the return of S&P 500 since 2002*. See the 10 stocks » *Stock Advisor returns as of November 18, 2024 Leo Sun has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Nvidia, Serve Robotics, and Uber Technologies. The Motley Fool recommends Nasdaq. The Motley Fool has a disclosure policy. Could Serve Robotics Become the Next Nvidia? was originally published by The Motley Fool Sign in to access your portfolio
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Solomon to Build Next Wave of Advanced Robotics Solutions Using …
Description: TAIPEI, July 1, 2024 /PRNewswire/ -- Solomon, a leader in advanced vision and robotics solutions, is excited to announce a collaboration with NVIDIA a...
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ABB Robotics: accordo di cessione fra ABB e SoftBank Group
Description: Ai clienti i vantaggi della combinazione tra la tecnologia e l'esperienza industriale di ABB Robotics e le conoscenze di SoftBank su AI, robotica e calcolo
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La cessione, per un valore d’impresa di 5,375 miliardi di dollari, riflette la solidità a lungo termine del business della robotica e crea valore immediato per gli azionisti di ABB ABB utilizzerà i proventi della cessione in linea con i propri principi di allocazione del capitale ABB ha annunciato di aver firmato un accordo per la cessione della propria divisione Robotics a SoftBank Group Corp. per un valore d’impresa di 5,375 miliardi di dollari, decidendo di non proseguire con la precedente intenzione di scorporare la divisione e quotarla come società separata. L’operazione è soggetta alle autorizzazioni regolamentari e ad altre condizioni di chiusura consuete ed è prevista la conclusione nella metà o fine del 2026. Peter Voser, presidente di ABB, ha dichiarato: “L’offerta di SoftBank è stata attentamente valutata dal Consiglio di Amministrazione e dal Comitato Esecutivo, e confrontata con la nostra intenzione originaria di procedere con uno spin-off. Essa riflette i punti di forza a lungo termine della divisione e la cessione creerà valore immediato per gli azionisti di ABB”. “ABB utilizzerà i proventi derivanti dalla transazione in linea con i propri consolidati principi di allocazione del capitale. Le nostre ambizioni per ABB restano invariate: continueremo a concentrarci sulla nostra strategia di lungo periodo, basata sulle nostre posizioni di leadership nei settori dell’elettrificazione e dell’automazione”. Morten Wierod, CEO di ABB, ha aggiunto: “SoftBank sarà un’eccellente nuova casa per l’attività e per i suoi dipendenti. ABB e SoftBank condividono la stessa visione: il mondo sta entrando in una nuova era della robotica basata sull’intelligenza artificiale, e crediamo che la divisione e l’offerta robotica di SoftBank possano insieme plasmare al meglio questa nuova era”. “ABB Robotics beneficerà della combinazione tra la propria tecnologia leader e la profonda esperienza industriale con le capacità avanzate di SoftBank in AI, robotica e calcolo di nuova generazione. Ciò permetterà all’attività di rafforzare ed espandere la propria posizione di leader tecnologico nel suo settore”. Masayoshi Son, presidente e CEO di SoftBank Group Corp. ha dichiarato: “La prossima frontiera di SoftBank è la ‘Physical AI’. Insieme ad ABB Robotics uniremo tecnologie e talenti di livello mondiale sotto una visione condivisa: fondere la Super Intelligenza Artificiale con la robotica, guidando un’evoluzione rivoluzionaria che farà progredire l’umanità”. A seguito della firma dell’accordo, ABB adeguerà la propria struttura di reporting, passando a tre aree di business. A partire dal quarto trimestre 2025, la divisione Robotics sarà riportata come “Attività cessate” (Discontinued Operations). La divisione Machine Automation, che attualmente insieme a Robotics costituisce l’area di business Robotics & Discrete Automation, entrerà a far parte dell’area Process Automation. Al momento della chiusura, la cessione comporterà una plusvalenza contabile pre-tasse non operativa di circa 2,4 miliardi di dollari, con proventi in cassa attesi, al netto dei costi di transazione, pari a circa 5,3 miliardi di dollari. I costi di separazione stimati sono di circa 200 milioni di dollari, circa la metà dei quali già inclusi nelle previsioni per il 2025. La stima attuale di ABB per i flussi fiscali in uscita legati alla separazione locale dell’attività si colloca tra i 400 e i 500 milioni di dollari. ABB Robotics è un leader nel proprio settore, al centro delle tendenze secolari e future dell’automazione. Come già comunicato, esistono sinergie limitate tra il business della robotica e il resto delle attività di ABB, caratterizzate da una diversa domanda e da differenti dinamiche di mercato. La divisione ABB Robotics impiega circa 7.000 persone. Con ricavi 2024 pari a 2,3 miliardi di dollari, ha rappresentato circa il 7% dei ricavi complessivi del Gruppo ABB, con un margine Ebita operativo del 12,1%. Comunicato ad hoc ai sensi dell’art. 53 del Regolamento di quotazione della Borsa svizzera SIX Fonte foto Pixabay_geralt Il nuovo Acopos M4 di B&R ridefinisce il motion control ad alte prestazioni per la produzione moderna. Combina precisione e potenza eccezionali con un design compatto e scalabile. Un nuovo sistema embedded consente l’implementazione della manutenzione predittiva,... A SPS 2025, B&R Industrial Automation, la divisione Machine Automation di ABB, presenterà una tecnologia rivoluzionaria nel campo del motion, progettata per aiutare i produttori a superare le principali sfide odierne: cicli di vita dei prodotti più... Con l’integrazione della divisione Machine Automation nell’area Process Automation, dove era confluita anche B&R, i clienti dei settori di processo e ibridi di ABB potranno beneficiare di sinergie tecnologiche più profonde, che integrano automazione, elettrificazione e digitalizzazione,... ABB Robotics ha investito nella californiana LandingAI per accelerare la trasformazione della visione AI, rendendola più rapida, intuitiva e accessibile a una platea più ampia di utenti. Questa collaborazione pionieristica integrerà le capacità di visione AI di... B&R, la Divisione Machine Automation di ABB, ha ottenuto la certificazione IEC 62443-4-1 per l’intero processo di sviluppo dei propri prodotti. L’audit condotto da TÜV Rheinland accerta la conformità delle pratiche di sviluppo agli standard internazionali per... La nuova piattaforma di motori a velocità variabile LV Titanium di ABB offre i vantaggi di un motore ad alta efficienza e della tecnologia di azionamento a velocità variabile in un’unica soluzione plug&play, compatta e personalizzabile, che... Start-up, scale-up e PMI italiane hanno tempo fino al 10 settembre 2025 per partecipare alla Call4Innovit, il programma promosso da Innovit, Italian Innovation and Culture Hub di San Francisco, nel cuore della Silicon Valley, dedicato a hardware, robotics, IoT,... Integrare soluzioni software MES avanzate e hardware industriale può trasformare un impianto in una fabbrica digitale altamente efficiente e affidabile Leggi l’articolo ABB Robotics sta preparando il futuro del fast food con BurgerBots, un innovativo concetto di ristorante lanciato a Los Gatos, in California. Progettata per offrire ogni volta hamburger perfettamente cotti e preparati su ordinazione, la cucina automatizzata... All’edizione 2025 di SPS Italia, Sick presenta un “impianto dimostrativo” di soluzioni applicative complete per la digitalizzazione, la robotica, la mobilità e la visione artificiale, pensate per rispondere concretamente alle esigenze del mercato e trasformare le sfide in realtà.... Il nuovo Acopos M4 di B&R ridefinisce il motion control ad alte prestazioni per la produzione moderna.... A SPS 2025, B&R Industrial Automation, la divisione Machine Automation di ABB, presenterà una tecnologia rivoluzionaria nel... L’ecosistema start-up italiano accelera sui numeri ma frena sui capitali: 204 round chiusi nel... Hexagon AB ha annunciato l’acquisizione di IconPro, azienda tedesca specializzata in soluzioni di intelligenza... Dopo le tappe di Roma, Cagliari e Torino, Milano ha ospitato ieri l’evento conclusivo... Per affrontare lo scenario competitivo odierno, sempre più sfidante e dinamico, le aziende industriali... Forte dell’esperienza ultradecennale nel mondo dell’automazione industriale e del monitoraggio energetico, Seneca propone soluzioni... La produzione di pneumatici è un processo altamente complesso e i sistemi utilizzati richiedono... Automazione Plus è un network di Quine. Quine srl Direzione, amministrazione, redazione, pubblicità Viale Enrico Forlanini 21 - 20134 MilanoTel. +39 02 864105 | Fax +39 02 72016740 | P.I.: 13002100157 Contatti: media.quine.it | www.quine.it | quineformazione.it Privacy Copyright 2024 - Tutti i diritti riservati
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SoftBank Invests $2.8 billion In Norwegian Robotics Firm AutoStore
Description: Japanese tech conglomerate SoftBank has acquired 40% of Norwegian warehouse automation firm AutoStore for $2.8 billion. The news was first reported by The Wall ...
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SoftBank Expands AI Footprint With Multibillion-Dollar Robotics Deal
Description: Tech conglomerate SoftBank Group has agreed to a $5.4 billion deal for the industrial-robotics-focused business of ABB, a bid to combine the potential of…
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ABB Robotics: Überraschender Kauf durch Softbank und zukünftige Ausrichtung
Description: ABB plant, seine Robotiksparte auszugliedern und an die Börse zu bringen. Überraschend wurde sie jedoch von Softbank gekauft. Was bedeutet das für die Zukunft von ABB Robotics?
Description: SoftBank Group announced on Monday that it has agreed to buy the robotics division of Swiss engineering firm ABB for $5.4 billion. The move is designed to
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SoftBank Group announced on Monday that it has agreed to buy the robotics division of Swiss engineering firm ABB for $5.4 billion. The move is designed to strengthen the Japanese company’s position in artificial intelligence. The deal, which is subject to regulatory approval, means ABB will no longer pursue its previous plan to spin off the robotics business into a separately listed company. The acquisition is a key part of SoftBank founder Masayoshi Son’s vision for “Physical AI,” which aims to combine advanced artificial intelligence with robotics. Don't miss out on the latest insights, trends, and analysis in the world of data, technology, and startups. Subscribe to our newsletter and get exclusive content delivered straight to your inbox. “SoftBank’s next frontier is Physical AI. Together with ABB Robotics, we will unite world-class technology and talent under our shared vision to fuse Artificial Super Intelligence and robotics — driving a groundbreaking evolution that will propel humanity forward.” Son defines Artificial Super Intelligence (ASI) as AI that is 10,000 times smarter than humans. He has worked to position SoftBank at the center of the AI industry through investments and acquisitions. The company owns chip designer Arm, holds a major stake in OpenAI, and has previous robot-related investments in companies like AutoStore Holdings and Agile Robots. This is not the company’s first venture into robotics. In 2012, SoftBank acquired a majority stake in a French company called Aldebaran, which led to the launch of the humanoid robot Pepper. Although that project did not succeed commercially, robotics has re-emerged as a key focus for the company. For ABB, the sale is a strategic shift from its previous plan to spin off the robotics unit. The company stated that the deal “will create immediate value to ABB shareholders” and that it will use the proceeds from the transaction according to its established capital allocation principles. ABB expects to receive approximately $5.3 billion in cash proceeds from the sale. The expected separation cost is around $200 million. Featured image credit
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SoftBank buys ABB robotics unit for $5.4 billion, bets on …
Description: SoftBank buys ABB robotics unit for $5.4 billion, bets on “physical AI” SoftBank announced the acquisition of ABB Group’s robotics business for $5.375 bil...
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Wall Street Pit - Finance, Stock Market, Technology, Science
