Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions

2026-05-25

AI Summary

This work addresses the challenge of ensuring safety for humanoid robots operating in complex environments under model uncertainty, trajectory errors, and external disturbances, where constraints such as joint limits, self-collision avoidance, and obstacle clearance must be rigorously enforced. To this end, the authors propose a hierarchical safety-critical whole-body control framework that, for the first time, integrates input-to-state safe control barrier functions (ISSf-CBFs) into humanoid robot control. The framework synergistically combines kinematic planning, ISSf-CBF-based safety filtering, and dynamic tracking to enable real-time safety corrections and guarantee dynamic feasibility even under perturbations. Experimental results demonstrate that the approach significantly enhances safety margins under model mismatch and reliably enforces diverse safety constraints across tasks including walking, teleoperation, and single-leg balancing.

Abstract

Safety-critical control is essential for humanoid robots operating in complex human-centered environments, where physical safety constraints such as joint limits, self-collision avoidance, obstacle avoidance, and workspace boundaries must be satisfied during real-robot operation. However, existing approaches remain limited because kinematic safety guarantees can be degraded in the presence of unknown disturbances, such as model uncertainties, trajectory-tracking errors, and external perturbations. This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots based on input-to-state safe control barrier functions (ISSf-CBFs). The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC). KinWBC generates nominal joint-motion references from prioritized tasks; the ISSf-CBF filter minimally modifies these references to satisfy kinematic safety constraints under bounded disturbances; and DynWBC tracks the filtered references while enforcing full-body dynamic feasibility and contact stability. Safety constraints are imposed on a whole-body kinematic model, and the ISSf-CBF parameters are conservatively tuned so that the resulting kinematic safety guarantees can be transferred to full-order humanoid dynamics under unknown disturbances. Simulation and real-robot experiments demonstrate that the proposed framework improves safety margins under model mismatch and reliably enforces multiple safety constraints in real time during locomotion, teleoperation, and single-leg balancing with hand control. Project website: https://kwlee365.github.io/SafeWBC-Website/

Problem

• humanoid robots
• safety-critical control
• kinematic safety constraints
• unknown disturbances
• whole-body control

Innovation

• Input-to-State Safe Control Barrier Functions
• Whole-Body Control
• Humanoid Robots
• Safety-Critical Control
• Disturbance Robustness