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Robotics news archive

Today 4 records
Today2026-10-04Sun
  3. IT Home — Robotics85

    Hyundai Plans to Deploy 25,000 Boston Dynamics Atlas Robots and Build a U.S. Factory with Annual Capacity of 30,000 Units

    Boston Dynamics and Hyundai Motor Group are collaborating to establish a robotics application center in Georgia, U.S., for testing and training Atlas robots. Hyundai plans to deploy 25,000 Atlas robots globally in the coming years and build a U.S. factory with an annual production capacity of 30,000 units. The Atlas robot has 56 degrees of freedom, a maximum lifting capacity of 50 kg, and a battery life of approximately 4 hours.

    Editorial context：The article details Hyundai's plan to deploy 25,000 Boston Dynamics Atlas robots globally and build a U.S. factory with annual production capacity of 30,000 units, highlighting the collaboration between Boston Dynamics and Hyundai Motor Group to integrate Atlas into manufacturing processes.

2026-10-03Sat
  2. High-tech Robotics — WeChat85

    A 2-Year-Old Embodied Intelligence Company Founded by Fei-Fei Li Sells for 55 Billion Yuan; Midea Group Plans to Acquire Welding Subsidiary for 6.64 Billion Yuan | Major Capital Events of the Week

    The article reports on major capital events in the recent robotics and embodied intelligence sectors, including AMD's acquisition of WorldLabs, a company co-founded by Fei-Fei Li, for approximately 55 billion yuan, Midea Group's plan to acquire a welding subsidiary for 6.64 billion yuan, and several robotics companies such as Benmo Technology, Huanchuang Technology, and Juxi Intelligent completing financing or going public. Additionally, multiple embodied intelligence products and solutions have been launched, covering humanoid robots, service robots, flexible electronic skin, tactile perception systems, and more, showcasing the industry's rapid progress in technology, capital, and application scenarios.

2026-10-02Fri
  1. Skild AI — Blog62

    Building the general-purpose robotic brain

    Skild AI introduces their omni-bodied robotics foundation model, the Skild Brain, which is trained across diverse morphologies and data sources to enable generalization across tasks and hardware. The model uses a hierarchical architecture with low- and high-frequency policies, and is pre-trained using large-scale simulation and internet video data, with post-training on real-world data. This model represents a significant step toward creating a general-purpose robotic brain capable of operating on various robot types.

    Editorial context：Skild AI introduces their omni-bodied robotics foundation model, the Skild Brain, which is trained across diverse morphologies and data sources to enable generalization across tasks and hardware. The model uses a hierarchical architecture with low- and high-frequency policies, and is pre-trained using large-scale simulation and internet video data, with post-training on real-world data. This model

  2. Skild AI — Blog85

    The case for an omni-bodied robot brain

    Skild AI presents a research paper detailing the development of an AI model trained across a vast array of robot bodies, enabling it to adapt to unpredictable scenarios without prior exposure. The model demonstrates zero-shot control and in-context learning, showing resilience in scenarios like limb loss, joint failure, and morphological changes. The work highlights the importance of adaptability in embodied AI for real-world applications.

    Editorial context：This research introduces an 'omni-bodied' AI model trained across 100,000 different robot bodies, demonstrating zero-shot adaptation to extreme morphological changes through in-context learning. The approach emphasizes the need for AI to adapt rather than memorize, drawing parallels to biological evolution and AGI development.

  7. arXiv Robotics — research abstracts75

    Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining

    The paper presents HumanVerse-500, a 500-hour dataset of human loco-manipulation behaviors collected with a lightweight wearable system. It introduces λ₀, a whole-body humanoid vision-language-action policy trained through three stages, achieving state-of-the-art performance on real-world tasks and analyzing how human data supports downstream control.

  8. arXiv Robotics — research abstracts63

    Whole-Body Aerial Grasping and Lifting via Partial Visual Observations

    This research presents a recurrent teacher-student framework that learns a single policy in simulation to jointly command flight, arm motion, and gripper closure without explicit task-phase input. The method achieves high success rates in simulation under nominal, physics/control-randomized, and additional camera-randomized conditions.

  9. arXiv Robotics — research abstracts62

    DeepJEPA: Scaling World Models from Within

    This research introduces DeepJEPA, a weight-tied joint-embedding predictive world model that scales computation internally by focusing on decision-critical transitions. It outperforms or matches fixed-depth planners in five visual-control settings while using fewer updates per transition, demonstrating that strategic allocation of internal computation improves planning without requiring uniform state decodability.

  10. arXiv Robotics — research abstracts66

    DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation

    This research presents DexPolicy, a reinforcement learning approach that explicitly manages exploration scale during training to enhance dexterous manipulation. The method is tested across multiple policy optimization settings (PPO, GRPO, FPO) and shows significant improvements in task success rates, with FPO achieving the highest performance on both simulated and real-world robotic arms.

  11. arXiv Robotics — research abstracts61

    IndoorBEV: A Lightweight Real-Time LiDAR BEV Perception System for Indoor Mobile Robots

    IndoorBEV is a lightweight LiDAR perception framework that addresses the challenge of efficient indoor perception by integrating height-aware BEV representations and geometry-conditioned feature fusion. It achieves 0.6M parameters, 2.3 MB storage, and 169.6 ms latency on an NVIDIA AGX Orin, with a 1.8% deadline miss ratio.

  12. arXiv Robotics — research abstracts64

    Real-Time Whole-Body Safe Motion Generation for Multi-Segment Tendon-Driven Continuum Robots

    This research presents a unified actuation-space framework for planar multi-segment tendon-driven continuum robots, combining energy-based modeling with constraint satisfaction techniques to achieve real-time whole-body safe motion generation. The method achieves collision-free success rates of 96% and 100% in static and dynamic scenarios, respectively, compared to 60% and 80% without CBF constraints. The model closely matches GVS references with a maximum curvature error of $5.223 imes 10^{-2} ext{m}^{-1}$, and demonstrates real-time performance with a mean control-step time of 6.66 ms.

  17. RoboSpeak — WeChat85

    Rather Melt Than Sell: What Is Figure AI Afraid Of?

    To address the issue of insufficient warehouse space, Figure AI decided to melt down the F.02 robot instead of disassembling or selling it. This decision involves multiple considerations, including intellectual property protection, technical confidentiality, and asset management. Although the destruction process faced challenges in terms of site and technology, it was ultimately completed at the Finnish factory. This incident has also sparked discussions about the management of retired robots and industry standards.

    Editorial context：The destruction of Figure AI's F.02 robot reflects the complexity of robot decommissioning management, involving multiple considerations such as intellectual property protection, asset management, technical confidentiality, and industry standards. The choice of destruction method is not only related to technical implementation but also reflects the company's emphasis on data security and commercial confidentiality.

2026-10-01Thu
  1. Open Robotics15

    Jishnu is right! We need your feedback to build a better open source community. We're also giving away free ROS and Gazebo swag. https://go.openrobotics.org/orsurvey26

    Quoted postJishnu ⚡@lonebots

    Go do your part. Help improve the robotics community and the tools we all depend on. @OpenRoboticsOrg is running its 2026 annual survey, and they want input from everyone in robotics — whether you use ROS or not. More details: https://discourse.openrobotics.org/t/complete-the-open-robotics-annual-survey-2026-to-enter-the-prize-draw/58488

  3. Sanctuary AI15

    Robotic Dexterity Explained | "What is robotic dexterity?" In Part 1 of Dexterity Explained, we break down what manual dexterity is, why it matters, and how robotic hands and Physical AI work together to bring greater skill, precision, and control to physical tasks. The more dexterous robots become, the more complex industrial work they can take on. Learn more about our dexterous solutions: https://sanctuary.ai/solutions/ #Explained #PhysicalAI #Dexterity #Robotics #RobotHand #SanctuaryAI

  7. RoboSpeak — WeChat55

    Embodied Intelligence in Real Scenarios, Labor Most Glorious! Media Briefing for 2026 Global Developer Pioneer Conference and International Embodied Intelligence Skills Competition

    The media briefing for the 2026 Global Developer Pioneer Conference and International Embodied Intelligence Skills Competition took place on September 29 at the Zhangjiang Modility Community. The event focused on the application of embodied intelligence technology in real-world scenarios. The conference aims to promote the large-scale implementation of embodied intelligence technology in industries such as manufacturing, services, and special operations through simulated competitions, online simulations, and real-world competitions. The event attracted wide participation from government agencies, enterprise representatives, and developer communities, and the main competition is planned to be held from October 23 to 25 at the Zhangjiang Science Hall in Shanghai.

  8. RoboSpeak — WeChat86

    Motors Don't Need Upgrades, Robots Can Run Faster! Beihang University Science Subjournal Reveals New Logic for Quadruped Locomotion

    Professor Shi Qing's team from Beijing Institute of Technology was inspired by the high-speed running mechanism of the elephant shrew. They designed a micro quadruped robot named FLEXOR equipped with a dual-joint coupled spine. Through dynamic spine-leg coordination, the robot achieved a 31.6% increase in speed and a 32.2% reduction in energy consumption without upgrading its motors. The study reveals the critical role of the timing coordination between the spine and legs in locomotion performance and verifies the universality of this mechanism across different robot morphologies, providing new insights for highly mobile embodied intelligent robots.

2026-09-30Wed
  7. QbitAI — Robotics85

    GPT-6 Astra Connects to Unitree G1, Cleans the Kitchen!

    The Stanford team's HomeBody project demonstrates how GPT-6 Astra controls the Unitree G1 robot to complete kitchen cleaning tasks through a three-step process: spatial exploration, digital twin simulation, and task execution. The system uses pre-trained models and a skill library to perform complex operations in new environments without additional training.

    Editorial context：The article explains how GPT-6 Astra controls the Unitree G1 robot to perform kitchen tasks using a three-step process: spatial exploration, simulation mapping, and task execution. It highlights the integration of perception, planning, and control systems, and discusses the role of pre-trained models and skill-based execution in enabling generalization across new environments.

  8. RoboSpeak — WeChat85

    Deployment Year Begins: Zhixuan Deploys 100 Robots in Retail Stores

    Zhixuan Robotics has deployed 100 units of the Lingxi X2 humanoid robot in 100 retail stores of Aishida, achieving a 39% increase in store sales. The robots, named 'Aibao,' engage customers by understanding their cooking needs and guiding them toward suitable products, while also seamlessly transferring conversations to human staff when necessary. This marks a milestone in the deployment of embodied AI in real-world retail environments.

    Editorial context：This report highlights a significant deployment of humanoid robots in retail environments, showcasing how they integrate with human staff to improve sales performance. The case study with 100 robots in 100 stores demonstrates the practical application of embodied AI in real-world commerce, emphasizing the importance of collaboration between robots and human workers.

  9. RoboSpeak — WeChat86

    Humanoid Robot Patents: China Claims 60% Share, What Next?

    The article analyzes China's leading position in the field of humanoid robot patents, noting that as of the first half of 2026, China holds 60.8% of relevant patents, significantly surpassing the United States and South Korea. The article examines the evolution of patent strategies, the trend of technological shift from hardware to software, and the progress made by Chinese companies in patent quality, application scenarios, and global layout. It also points out that patent quantity does not equate to comprehensive technological leadership, emphasizing the challenges of manufacturing costs and quality, as well as the dual role of patents in industrial competition.

    Editorial context：The article presents a comprehensive analysis of China's growing dominance in humanoid robot patents, highlighting the shift from hardware to software innovation and the implications for global competition. It underscores the importance of patent strategy in shaping the future of the industry, while also acknowledging the challenges in translating patent strength into commercial success.

  10. arXiv Robotics — research abstracts62

    DORA: Divergence-Oriented Data-Relay Algorithm for Partially Connected Robot Teams

    This research presents DORA, a divergence-oriented data-relay algorithm that enhances communication in partially connected UAV teams by prioritizing the value of information to the team. The algorithm quantifies mission-relevant divergence between a robot's information state and its teammates' knowledge, improving MRT resolution delay by up to 74.8% over traditional methods.