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

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. A centroidal angular momentum task is also used to generate arm motions that assist humanoid locomotion and disturbance recovery. Walking experiments show that CBFs allow the centroidal angular momentum task to generate feasible arm motions and avoid leg self-collisions when the footstep location or swing trajectory provided by the high-level planner are infeasible for the real robot.