提出新算法让人形机器人在复杂环境里避障更安全且不卡死。
Dexterous Safe Control for Humanoids in Cluttered Environments via Projected Safe Set Algorithm
- 通过投影安全集算法智能放松冲突约束，避免控制失效。
- 实测在真实机器人上实现零参数调整即适配多种避障任务。
- 适合做高精度人形机器人操作与复杂场景部署的研究者。
在真实应用场景中，确保人形机器人安全至关重要，同时不能牺牲性能。本文研究了细粒度安全问题，即在杂乱环境中对肢体进行精细几何约束，以避免外部碰撞和自碰撞。相较于稀疏环境中使用简化包围体的安全方法，细粒度安全会产生大量约束，常导致求解时约束不可行。为此，我们提出投影安全集算法（p-SSA），扩展经典安全控制算法以应对多约束情形。p-SSA以合理方式松弛冲突约束，最小化安全违规，保障机器人控制可行性。我们在仿真和真实单位方舟G1人形机器人上验证了该方法，结果表明，p-SSA使机器人在复杂情境下稳健运行，安全违规极小，并能直接泛化至多种任务而无需参数调整。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。