Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions
For humanoid robotics, this work addresses the safety problem in motion imitation by integrating collision avoidance, but it is incremental as it applies existing CBF methods to a specific application.
This paper presents a vision-based framework for human-to-humanoid motion imitation that uses a Control Barrier Function layer to filter commands, preventing self-collisions and human-robot collisions. Simulation results validate real-time collision-aware imitation.
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.