Humanoid Self-Collision Avoidance Using Whole-Body Control with Control Barrier Functions

TL;DR

This paper presents a control approach combining control barrier functions with whole-body control to enable real-time self-collision avoidance in humanoid robots, ensuring collision-free and feasible motions during complex tasks.

Contribution

It introduces a novel CBF-WBC controller that considers the robot's full dynamics for guaranteed self-collision avoidance, validated through simulation and walking experiments.

Findings

Enables the robot's hand to avoid infeasible collision trajectories.

Prevents leg self-collisions during walking with high-level planner inputs.

Improves disturbance recovery with feasible arm motions.

Abstract

This work combines control barrier functions (CBFs) with a whole-body controller to enable self-collision avoidance for the MIT Humanoid. Existing reactive controllers for self-collision avoidance cannot guarantee collision-free trajectories as they do not leverage the robot's full dynamics, thus compromising kinematic feasibility. In comparison, the proposed CBF-WBC controller can reason about the robot's underactuated dynamics in real-time to guarantee collision-free motions. The effectiveness of this approach is validated in simulation. First, a simple hand-reaching experiment shows that the CBF-WBC enables the robot's hand to deviate from an infeasible reference trajectory to avoid self-collisions. Second, the CBF-WBC is combined with a linear model predictive controller (LMPC) designed for dynamic locomotion, and the CBF-WBC is used to track the LMPC predictions. Walking experiments show that adding CBFs avoids leg self-collisions when the footstep location or swing trajectory provided by the high-level planner are infeasible for the real robot, and generates feasible arm motions that improve disturbance recovery.