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

arXiv: https://arxiv.org/abs/2207.00692 · HTML: https://mlsanity.com/paper/2207.00692/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2022.

## Built on by (most cited first, 5 of 5)

- [AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents](https://mlsanity.com/paper/2403.12835/) (2024)
- [Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control](https://mlsanity.com/paper/2510.12717/) (2025)
- [Effective Virtual Reality Teleoperation of an Upper-body Humanoid with Modified Task Jacobians and Relaxed Barrier Functions for Self-Collision Avoidance](https://mlsanity.com/paper/2411.07534/) (2024)
- [Constrained Whole-Body Tracking for Humanoid Robots](https://mlsanity.com/paper/2606.00374/) (2026)
- [ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids](https://mlsanity.com/paper/2609.15988/) (2026)

## Abstract (arXiv)

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.