Acc-CBF-QP: Acceleration-Based Safety Filter for RL Robotic Control
Researchers present Acc-CBF-QP, an acceleration-based Quadratic Program safety filter leveraging Control Barrier Functions to enforce safety constraints on reinforcement learning (RL) policies in real time, without altering the training process. The method is demonstrated on both a Kinova Gen3 manipulator and a Unitree H1 humanoid robot, achieving up to 92% reduction in constraint violations on hardware and fully eliminating violations on the Kinova Gen3. The approach maintains nominal RL task performance in safe regimes and prevents constraint-induced shutdowns under aggressive commands.
Why it matters: This work offers a practical and effective solution for safely deploying RL policies on real-world robots, addressing a major challenge in robotic control.
Full story at: arXiv Robotics ↗