How do we make RL-powered humanoid robots safe without sacrificing performance? In our latest #CoRL2026# paper, Constrained Whole-Body Tracking for Humanoid Robots, we introduce safety filters that enforce kinematic and dynamic constraints—even those never encountered during training. The approach minimally interferes with the underlying RL policy and requires *no retraining*. At CoRL in Austin? Come meet @danielpmorton at Poster Session 2, Tuesday evening, #204#! Project: Paper: Work led by @danielpmorton, in collaboration with Pranit Mohnot #HumanoidRobotics# #ReinforcementLearning# #RobotLearning# #PhysicalAISafety# #Robotics# @StanfordAILab @StanfordASL

Human motion. Human touch. A new chapter begins. Two next-generation products from debuting at CoRL 2026: 🔹 DAS Ego 2.0 — A first-person multimodal data collection system with hand-tracking precision within 4 mm. 🔹 DAS Dex 2.0 — A high-fidelity human dexterity data glove with e-skin tactile sensing. From whole-body motion to tactile sensing, perfectly in sync. Come experience it live. See you at Booth #200#! 📅 November 9–11, 2026 📍 110 E 2nd St, Austin, TX 78701, USA #CoRL2026# #GenRobotAI# #Robotics# #EgoData# #WholeBodyManipulation# #DexterousManipulation# #EmbodiedAI# #RobotLearning#

CoRL 2026 Oral 🌟 A team led by Peking University introduced OpenDexGrasp. It takes free-form language instructions, multi-view images, and 3D point clouds as input. Then it directly generates a dexterous five-finger grasp pose. For example, grip a hammer by its handle, not its head. To train the model, the team built OpenDexVerse: • OpenDex-Scale: 1.24M automatically generated grasp samples. • OpenDex-Align: 27.42K human-teleoperated and transferred grasp samples. The results: In simulation: 62.96% functional grasp success on unseen object categories. The DexGraspNet 2.0 baseline scored 43.22%. On a real robot, it achieved a 72% success rate across 10 everyday objects, including brushes, shampoo bottles, umbrellas, and pitchers. The baseline scored 59%. It's also faster. Traditional methods predict the graspable area first, then optimize the hand pose. OpenDexGrasp generates the grasp pose directly. Traditional pipeline: 2.84 s OpenDexGrasp: 0.93 s 67% less inference time. It's not perfect, though. Thin or complex objects like keyboards, frisbees, and flower pots can still cause problems, including finger penetration and inaccurate grasp placement.