通过投影安全集算法实现杂乱环境中人形机器人的灵巧安全控制
Dexterous Safe Control for Humanoids in Cluttered Environments via Projected Safe Set Algorithm
AI总结：
本文针对人形机器人在杂乱环境中的灵巧安全控制问题，提出投影安全集算法（p-SSA），通过有原则地松弛冲突约束来保证控制可行性，并在仿真和 Unitree G1 实机上验证了其稳健性与零参数调优的泛化能力。
摘要：
确保人形机器人在实际应用中的安全性且不牺牲性能至关重要。本文研究灵巧安全问题，其特点是采用肢体级几何约束，以在杂乱环境中同时避免外部碰撞和自碰撞。与在稀疏环境中使用简化包围几何体的安全方法相比，灵巧安全会产生大量约束，在求解安全机器人控制时往往导致约束集不可行。为解决这一问题，我们提出了投影安全集算法（p-SSA），这是经典安全控制算法在多约束情形下的扩展。p-SSA 以有原则的方式松弛相互冲突的约束，最小化安全违规，从而保证机器人控制的可行性。我们在仿真中和真实的 Unitree G1 人形机器人上验证了该方法，机器人执行复杂的避碰任务。结果表明，p-SSA 使人形机器人能够在具有挑战性的情况下稳健运行，安全违规最小，并且无需任何参数调节即可直接泛化到各种任务。
英文摘要：
It is critical to ensure safety for humanoid robots in real-world applications without compromising performance. In this paper, we consider the problem of dexterous safety, featuring limb-level geometry constraints for avoiding both external and self-collisions in cluttered environments. Compared to safety with simplified bounding geometries in sprase environments, dexterous safety produces numerous constraints which often lead to infeasible constraint sets when solving for safe robot control. To address this issue, we propose Projected Safe Set Algorithm (p-SSA), an extension of classical safe control algorithms to multi-constraint cases. p-SSA relaxes conflicting constraints in a principled manner, minimizing safety violations to guarantee feasible robot control. We verify our approach in simulation and on a real Unitree G1 humanoid robot performing complex collision avoidance tasks. Results show that p-SSA enables the humanoid to operate robustly in challenging situations with minimal safety violations and directly generalizes to various tasks with zero parameter tuning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
