SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
For humanoid robot safety, SHIELD provides a minimally-invasive method to add formal safety guarantees to black-box learned controllers, addressing a key bottleneck in deploying RL policies on real hardware.
SHIELD adds a safety layer to existing learned locomotion controllers on humanoids, using a stochastic dynamics model and discrete-time CBFs to enforce safety constraints with probabilistic guarantees. Hardware experiments on a Unitree G1 humanoid demonstrated safe obstacle avoidance in varied environments without retraining the nominal RL controller.
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint satisfaction, remains challenging for such policies. Reinforcement learning (RL) embeds constraints heuristically through reward engineering, and adding or modifying constraints requires retraining. Model-based approaches, like control barrier functions (CBFs), enable runtime constraint specification with formal guarantees but require accurate dynamics models. This paper presents SHIELD, a layered safety framework that bridges this gap by: (1) training a generative, stochastic dynamics residual model using real-world data from hardware rollouts of the nominal controller, capturing system behavior and uncertainties; and (2) adding a safety layer on top of the nominal (learned locomotion) controller that leverages this model via a stochastic discrete-time CBF formulation enforcing safety constraints in probability. The result is a minimally-invasive safety layer that can be added to the existing autonomy stack to give probabilistic guarantees of safety that balance risk and performance. In hardware experiments on an Unitree G1 humanoid, SHIELD enables safe navigation (obstacle avoidance) through varied indoor and outdoor environments using a nominal (unknown) RL controller and onboard perception.