OpenClaw is bringing its three-day Claw Hack hackathon and ClawCon conference to Shenzhen on December 4–6, 2026, following its Shanghai event. Claw Hack spans software and hardware tracks, with M5Stack, Seeed Studio's reBot Arm B601 and Pamir hardware, and applications close November 25. ClawCon runs December 6 with talks on open-source AI, open-weight models and running agents locally and in the cloud.
Preferred Networks' AUTiv automated mouse tail-vein injection system received the Japan Machinery Federation Chairman's Prize at the 12th Robot Award. The system uses 4K stereo cameras and deep learning to locate the roughly 0.3mm tail vein and performs automated puncture and dosing, reducing needle-stick accidents and animal stress. About 20 units have been delivered in Japan, with additional manufacturing confirmed.
LiveKit released LiveKit Portal for ROS 2, a standalone ROS 2 node that connects a ROS graph to a LiveKit room so ROS and non-ROS participants can exchange topics, service calls, and video over WebRTC without exposing DDS across networks. It supports four data paths—topics via DataTracks, services via RPC, images as VideoTracks, and direct video sources—with YAML rules for direction, rate limits, and schema encoding. A demo offloads stereo SLAM and scene reconstruction from a Jetson Orin Nano rover to a desktop RTX 5090.
Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model to date, which expands the range of skills a robot can learn and speeds up how fast it acquires them. Evaluated across 200 distinct tasks, the model achieves a 30% to 100% higher success rate than previous baselines while completing tasks 20% to 30% faster, and in a Mercedes-Benz production-line proof-of-concept, Apptronik's Apollo 2 humanoid picked and packed automotive parts with a 96% success rate. The release also adds inference-time reasoning guidance for physical safety, which raised safe task completion by 33% around obstacles and 43% around nearby people, plus ASIMOV-Jailbreak, an open-source benchmark on which Gemini 4 Argon reaches 99.5% jailbreak resistance.
Why it matters: Gemini Robotics 2.5 improved its success rate on 200 tasks by 30% to 100%, and achieved a 96% pick-and-place success rate in a pilot on the Mercedes-Benz production line, providing reference data for evaluating the real-world deployment of VLA models.
NVIDIA introduced GR00T 2, an open world action model built on Cosmos and trained on 100,000 hours of human egocentric data, alongside Isaac Lab 3.0 and new robotics benchmarks at CoRL. GR00T 2 completed 40 of 50 evaluated tasks at least once without task-specific demonstrations and reached 71% average success across 50 tasks with one demonstration each, rising to 95% with five. Isaac Lab 3.0 adds Newton physics integration for deformable objects and reports up to 2.5x the training throughput of Isaac Lab 2.3 with PhysX in a vision-based dexterous manipulation benchmark.
Why it matters: NVIDIA's CoRL release shows how simulation plus world action models now target delicate assembly work, with GR00T 2 and Isaac Lab 3.0 details worth comparing against prior robotics stacks.
UC Berkeley's BAIR is presenting 33 main-conference papers, 4 late-breaking posters, 3 orals and 7 spotlights at CoRL 2026, held November 9–12 in Austin, Texas. Professor Negar Mehr delivers an Early Career Keynote on how robots learn to interact and coordinate with others. Work spans embodied memory for vision-language-action models, dexterous manipulation scaling with egocentric human data, and agentic robot self-improvement.
NVIDIA is helping advance Korea's physical AI ecosystem, with Samsung, Kakao Mobility and LG Electronics applying accelerated computing, AI and simulation to semiconductor manufacturing, autonomous vehicles and robotics. Samsung's sMOL platform ran selected quantum chemical calculations up to 5x faster with NVIDIA cuEST, and one AI homogenization case cut simulation time from about 18 hours on a 32-core CPU to 41 seconds. Kakao Mobility plans a level 4 autonomous driving system on NVIDIA DRIVE AGX Thor, while LG demonstrated NVIDIA Isaac GR00T-powered manipulation with its CLOiD robot in a real manufacturing environment.
Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop, as the first of its FoundationWorlds for controlled robot learning and evaluation. The framework auto-generates skills and demonstrations via RL and motion planning in Isaac Lab, reaching up to 24x real-time on a single GPU, and ships with 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. In its experiments, ManiFlow's atomic-skill success was 80.0% in simulation and 81.1% on a real Franka FR3, while the Molmo 2 plus ManiFlow system succeeded in 55.5% of simulated and 50.0% of real-world episodes.
Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop, as the first of its FoundationWorlds for controlled robot-learning research. ToyBlocks auto-generates tasks and demonstrations via RL and motion planning in Isaac Lab, reaching up to 24 times real-time data generation on a single GPU, and ships with 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. In real-world tests on a Franka FR3, ManiFlow's atomic-skill success was 80.0% in simulation and 81.1% on hardware, while the Molmo 2 plus ManiFlow system succeeded in 55.5% of simulated and 50.0% of real episodes.
Markov Robotics is training a world model for zero-shot general-purpose robot dexterity, using fewer than ten hours of robot data where comparable results typically need thousands of hours. The company runs nearly all its compute on Modal, from preprocessing hundreds of terabytes of video to 2–10 concurrent training runs and remote inference via Modal Tunnels at roughly 20 milliseconds round-trip latency. Preprocessing that could take a full day completes in twenty to thirty minutes, with fan-out to upwards of 500 GPUs at once.
Physical Intelligence (Pi) runs its Visual-Language-Action robot models on Modal, adding only ~10-15ms of network overhead. Pi worked with Modal to build a QUIC-based portal over UDP with automatic NAT traversal, replacing TCP Tunnels to avoid request-response stalls in the robot control loop. Checkpoints stored in Modal Volumes load in under 30 seconds, and region-pinned deployments keep inference close to robots.