用视觉实现人形机器人安全模仿动作，实时防碰撞
Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions
- 单目摄像头捕捉人体关键点，转为关节角进行动作重定向
- 通过二次规划的控制屏障函数层过滤指令，避免自碰与人机碰撞
- 仿真验证了框架在实时性与安全性上的有效性，适合机器人交互场景
确保操作安全对人形机器人模仿人类动作至关重要。本文提出一种基于视觉的框架，使类人机器人能在模仿人类动作的同时避免碰撞。通过单个摄像头捕捉人体骨骼关键点，并将其转换为关节角度以实现动作重定向。安全通过控制屏障函数（CBF）层保障，该层以二次规划（QP）形式实现，可过滤模仿指令，防止自碰撞及人机碰撞。仿真结果验证了所提框架在实时碰撞感知动作模仿中的有效性。
原文摘要 · Abstract (English)
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.
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