Introducing ConstrainedMimic ( — a control framework for #humanoid# robot #safety# that enables real-time constraint enforcement within #RL-based# motion tracking policies by leveraging whole-body kinematics and dynamics. Recent advances in reinforcement learning have unlocked remarkable whole-body agility for humanoid robots. However, ensuring safety and satisfying constraints—especially those introduced after training—remains a significant challenge for deploying safe and reliable systems. ConstrainedMimic addresses this challenge by combining ideas from operational space control and control barrier functions (CBFs). The framework enables enforcement of arbitrary runtime constraints while preserving the ability of the policy to track complex motions. Importantly, constraints can be imposed on both the kinematic reference motion and the underlying robot dynamics, providing a principled approach to safer, more robust, and more controllable humanoid behavior. As #PhysicalAI#, #humanoid# #robotics#, and #embodied# #AI# systems move from research environments into the real world, the ability to guarantee safety and respect operational constraints will become increasingly important — ConstrainedMimic is a step in this direction. 📄 Paper: 💻 Code: Coming soon Great work led by @danielpmorton . #PhysicalAI# #AISafety# #HumanoidRobotics# #EmbodiedAI# #ReinforcementLearning# #Robotics# @StanfordAILab @StanfordEng