Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid robot to imitate human movements while avoiding collisions. Human skeletal keypoints are captured by a single camera and converted into joint angles for motion retargeting. Safety is enforced through a Control Barrier Function (CBF) layer formulated as a Quadratic Program (QP), which filters imitation commands to prevent both self-collisions and human-robot collisions. Simulation results validate the effectiveness of the proposed framework for real-time collision-aware motion imitation.
@article{arxiv.2604.11447,
title = {Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions},
author = {Wenqi Cai and John Abanes and Nikolaos Evangeliou and Anthony Tzes},
journal= {arXiv preprint arXiv:2604.11447},
year = {2026}
}