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.
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.
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.
The article explores the issue of data scarcity in the field of embodied intelligence, pointing out that although there are already a large number of data collection centers, high-quality and reusable data remains severely insufficient. The article analyzes structural contradictions such as high data collection costs, inconsistent quality, fragmented formats, and lack of cross-ontology reusability. It introduces Wu Wen Tech's solution of building a data foundation through a Real2Sim2Real closed-loop system, including large-scale data collection, automated annotation, and simulation training technologies, ultimately forming a data-driven flywheel to promote the development of embodied intelligence.
Editorial context：The article highlights the critical data scarcity in embodied AI, emphasizing the gap between the scale of robot models and the availability of high-quality physical interaction data. It identifies structural issues such as high collection costs, quality inconsistencies, and lack of standardization as major barriers. The solution proposed by Wu Wen Tech involves a Real2Sim2Real closed-loop system,
IEEE Spectrum rounds up robotics videos, including a Skydio fixed-wing drone using a robot arm for launch and capture, humanoid performances, and underwater manipulation. The roundup does not establish autonomous operation for the humanoid performance.
Editorial context：The event highlights the growing use of humanoid robots in large-scale public performances, demonstrating real-time autonomy and coordination in a high-visibility setting.
China is pursuing a strategic roadmap through its Five-Year Plans to dominate the global robotics industry, focusing on embodied AI and humanoid robotics. The 13th and 14th FYPs laid the foundation for industrial automation, while the 15th FYP emphasizes embodied intelligence, with state support for R&D and mass deployment of humanoids. Despite achievements in market share and production scale, challenges remain in precision hardware and semiconductor self-sufficiency, with risks of overcapacity and market volatility.
Editorial context：This analysis provides a detailed overview of China's strategic approach to robotics through its Five-Year Plans, highlighting the shift from industrial automation to embodied AI and the challenges in achieving self-sufficiency in critical components. It emphasizes the role of state coordination, domestic manufacturing capabilities, and the risks associated with the rapid expansion of humanoid机器人.
MIT researchers have developed a reconfigurable robotic lab that autonomously assembles, tunes, and dismantles optical experiments. The system uses robotic arms, 3D-printed component housings, and a cloud-based interface to enable remote operation. It can build and fine-tune a laser cavity in under 30 minutes, demonstrating the potential for fully automated optical experiments.
Editorial context：This research introduces a reconfigurable robotic optics lab capable of autonomously assembling, tuning, and dismantling optical experiments with micron-scale precision. The system integrates robotic arms, 3D-printed component housings, QR code identification, and a cloud-based interface for remote operation. It demonstrates the potential for fully automated optical experiments, reducing manual t犯
MIT researchers have developed a new technique that helps generative AI models meet strict safety and task-specific constraints without sacrificing output quality. The method, called HardFlow, reformulates constraint satisfaction as a trajectory-optim, allowing models to explore more freely during generation while ensuring final compliance with hard constraints. It is tested on robotics, control, and computer vision tasks, consistently outperforming existing methods in constraint satisfaction and solution quality.
Editorial context：This research introduces HardFlow, a novel method for ensuring generative AI models meet strict safety and task-specific constraints without compromising output quality. By reformulating constraint satisfaction as a trajectory-optimization problem, the approach allows models to explore more freely during generation while guaranteeing final compliance with hard constraints. The method is tested on
U.S. robotics researchers and industry leaders are seeking a cohesive national strategy to sustain innovation, enhance domestic production, and address supply chain vulnerabilities. The article explores the role of federal funding, private investment, and emerging legislation in shaping the U.S. robotics landscape, while also highlighting challenges in domestic manufacturing and the geopolitical implications of robotics technology.
Editorial context：This article highlights the U.S. robotics industry's growing focus on national strategy, emphasizing the need for coordinated government, industry, and academic efforts to maintain technological leadership and secure domestic manufacturing capabilities. It outlines the role of federal agencies, private investment, and emerging legislative initiatives in shaping the future of robotics in the U.S.
Aletta, the first autonomous blood-draw device approved for use in the U.S., uses imaging and robotics to perform blood draws with high success rates, even in challenging cases. While it addresses staffing shortages in clinical labs, concerns about skin tone bias and sample quality persist, requiring further validation.
Editorial context：Aletta, the first autonomous blood-draw device authorized in the U.S., combines imaging and robotics to perform blood draws with high success rates, though concerns about skin tone bias and sample quality remain. The device addresses staffing shortages in clinical labs but requires further validation to ensure equitable performance across all demographics.
This release introduces the ability to export a scene and trajectory as a standalone archive for bit-exact replay, along with numerous bug fixes and new features such as applying external wrenches, morph pose offsets, and improved FEM vertex constraints. It also includes enhancements for offscreen rendering and cross-platform compatibility.
Editorial context：This release introduces key improvements for scene export, trajectory recording, and bug fixes, enhancing the usability and reliability of the Genesis framework for simulation and robotics research.
Researchers from MIT Lincoln Laboratory deployed sensors in the Arctic to monitor under-ice sounds and test through-ice communication using a magnetic modem. The study addresses challenges in extreme weather and highlights the importance of community collaboration and sensor deployment strategies for Arctic research.
Editorial context：This research focuses on through-ice communication and acoustic monitoring in the Arctic, using low-cost sensors and magnetic modem technology. The study highlights the challenges of deploying and retrieving equipment in extreme conditions and emphasizes the importance of community engagement and collaboration with local experts.
Skild AI introduces S1, a robotic foundation model that leverages in-context learning to execute complex, long-horizon tasks without post-training. This marks a significant shift from traditional fine-tuning approaches, enabling rapid deployment and reducing data requirements. The model demonstrates strong performance on unseen tasks, including plant potting, pancake cooking, and kit assembly, and shows robustness to perturbations and common-sense reasoning.
Editorial context：Skild AI introduces S1, a robotic foundation model that leverages in-context learning to execute complex, long-horizon tasks without post-training. This marks a significant shift from traditional fine-tuning approaches, enabling rapid deployment and reducing data requirements. The model demonstrates strong performance on unseen tasks, including plant potting, pancake cooking, and kit assembly, and
FedEx and Dexterity have expanded their collaboration to deploy Dexterity's Foresight world model and Mech trailer loading systems at the FedEx Hagerstown Hub. This marks a significant scaling of physical AI in logistics, enabling production at a larger operational scale with a focus on safety, consistency, and performance.
Editorial context：The deployment of Dexterity's Foresight world model and Mech trailer loading systems at the FedEx Hagerstown Hub marks a significant step in scaling physical AI for industrial logistics. This collaboration demonstrates the integration of real-time perception, planning, and execution in high-volume operations, with a focus on safety and efficiency.
MIT's JARVIS Challenge explored whether AI can transform complex engineering tasks like building jet engines. Students used AI tools to design, fabricate, and test small gas turbine engines, but found that human expertise and judgment remained critical. The challenge revealed that while AI can speed up design and testing, physical manufacturing and engineering experience are still essential. The study emphasizes the need for a balance between AI assistance and human oversight in engineering workflows.
Editorial context：The JARVIS Challenge at MIT demonstrates how AI can assist in complex engineering tasks like building jet engines, but highlights the critical role of human judgment and expertise. The study shows that while AI can accelerate design and testing, physical manufacturing and engineering experience remain essential. The balance between leveraging AI tools and maintaining human oversight is key to safe
Firefly Aerospace's Blue Ghost Mission 2 will deploy NVIDIA Jetson for on-orbit AI processing, enabling real-time lunar data analysis and reducing latency. This marks the first use of Jetson in lunar orbit, supporting scientific research and future lunar exploration.
Editorial context：NVIDIA Jetson's deployment in lunar orbit marks a significant step in edge AI for space applications, enabling real-time data processing and reducing reliance on Earth-based computation.
Skild AI has acquired Zebra Technologies' robotics division to deploy its omni-bodied brain across warehouses, aiming to unlock massive productivity gains and enhance end-to-end fulfillment processes. This acquisition integrates Zebra's Symmetry Fulfillment platform with Skild's foundation model, enabling cross-embodiment generalization and intelligent decision-making in logistics environments.
Editorial context：Skild AI's acquisition of Zebra Technologies' robotics division marks a significant step in deploying omni-bodied intelligence across warehouse environments. This move integrates advanced robotics platforms with Skild's foundation model, enabling cross-embodiment generalization without retraining. The collaboration aims to create a unified intelligence layer for logistics operations, enhancing end
Dexterity has deployed Instinct, a tactile intelligence system that enables robots to perform force-guided manipulation with zero retraining. Instinct reacts to contact forces in 0.5–2 ms, 10–100× faster than human touch, and adapts in real time to constrained placements. It integrates with Dexterity’s world model, Foresight, to update contextual understanding through contact data, achieving an 81% success rate in production with adaptive strategies.
Editorial context：Dexterity introduces Instinct, a tactile intelligence system that closes the gap between sensing contact and acting intelligently on it. It operates at 500–2,000 Hz, reacting in 0.5–2 ms, 10–100× faster than human touch. Proven in production with an 81% success rate and adaptive strategies for constrained placements, Instinct integrates with Dexterity’s world model, Foresight, to update contextual
This tutorial explores the challenges of deploying VLA models on embedded robotic systems, including dataset recording best practices, fine-tuning techniques for ACT and SmolVLA, and real-time performance optimization using the NXP i.MX 95 SoC. It emphasizes asynchronous inference and hardware-aware scheduling to improve control and reduce latency.
Editorial context：This guide provides hands-on best practices for deploying Vision-Language-Action (VLA) models on embedded platforms, emphasizing dataset recording, model fine-tuning, and real-time performance optimization. It highlights the importance of asynchronous inference and hardware-specific optimizations for achieving reliable robotic control.
