SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics

TL;DR

SHIELD is a safety framework for humanoid robots that combines learned stochastic dynamics models with control barrier functions to provide probabilistic safety guarantees during navigation.

Contribution

The paper introduces a layered safety approach that integrates learned dynamics residuals with stochastic CBFs, enabling safety guarantees without retraining the main controller.

Findings

Enables safe obstacle avoidance in hardware humanoid navigation.

Provides probabilistic safety guarantees balancing risk and performance.

Works with existing learned controllers without retraining.

Abstract

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.