Technical Directions
Robotics coverage relating to reinforcement learning.
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Google DeepMind has released Gemini Robotics 2, a new model that enables robots to perform complex tasks with whole-body intelligence, advanced dexterity, and multi-robot collaboration. The model supports real-time reasoning, on-device adaptation, and safety features for human-robot interaction.
NVIDIA has released an open-source, GPU-accelerated Medical Physics Simulation framework as part of its Isaac for Healthcare platform. This tool enables developers to model anatomy-device interactions, generate complex scenarios, and train robot policies in simulation, significantly reducing development time and improving regulatory readiness.
Boston Dynamics deployed their production Atlas robot at the FIFA World Cup 2026™ Norway-Brazil match, where it performed a ceremonial role and showcased its capabilities. The demonstration involved dynamic movement and precision, trained through reinforcement learning, and highlighted the robot's potential for industrial applications beyond entertainment.
Boston Dynamics explores how football can inform robot movement by using human demonstrations and reinforcement learning to train Atlas to perform a complex 'Ghost Rabona' kick, highlighting the intersection of sports and robotics in developing dynamic, whole-body control.
Helix 02, a single neural system that controls the full body directly from pixels, now performs end-to-end living room cleanup, including tasks like cleaning surfaces, handling flexible objects, and bimanual manipulation. The system learns new capabilities through data without requiring new algorithms or specialized engineering.
Dexterity introduces 'Transactable World Models' as a foundational component for Physical AI, emphasizing interpretability, physics integration, and real-time consistency. The approach treats world models as operators rather than data stores, enabling multi-agent coordination and robust manipulation in complex environments. This contrasts with traditional world models that focus on data compression and replay, lacking causal reasoning and physical grounding.
Hugging Face announces LeRobot v0.4.0, a major upgrade for open-source robotics with Dataset v3.0, new VLA models like PI0.5 and GR00T N1.5, and a plugin system for hardware integration. The release also adds support for LIBERO and Meta-World simulations, multi-GPU training, and a new Hugging Face Robot Learning Course.
Sanctuary AI has demonstrated advanced manipulation skills using reinforcement learning to train dexterous policies for their high-performance hydraulic hands. The system successfully reorients objects in the real world under gravity and with added weight, showcasing the effectiveness of their approach. This represents the first successful transfer of such policies to five-fingered hands, highlighting the company's proprietary technology and hardware capabilities.
Sanctuary AI has demonstrated industry-leading sim-to-real transfer of dexterous manipulation policies using their high-degree-of-freedom hydraulic hands. The company leverages NVIDIA Isaac Lab, an open-source framework for robot learning, to simulate dexterity-focused training environments. This approach enables efficient training of autonomous robots, with the ability to train thousands of hands simultaneously and accelerate the learning process through online reinforcement learning.
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