基于分层优化的腿足运动全身约束学习
Whole-Body Constrained Learning for Legged Locomotion via Hierarchical Optimization
AI总结：
本文提出一种基于分层优化的全身跟随器，将硬约束与软约束集成到强化学习框架中，以提升腿足机器人在复杂环境中的运动安全性，并在六足机器人上验证了其通过性和安全性。
摘要：
强化学习（RL）已在各种具有挑战性的环境中的腿足运动上展现出令人印象深刻的表现。然而，由于仿真到现实的差距和可解释性的缺乏，部署在真实世界中的无约束RL策略仍然面临不可避免的安全问题，例如关节碰撞、过大的扭矩，或在低摩擦环境中的足部打滑。这些问题限制了其在具有严格安全要求的任务中的使用，例如行星探索、核设施检查和深海作业。在本文中，我们设计了一个基于分层优化的全身跟随器，它将硬约束和软约束集成到RL框架中，使机器人能够以更好的安全保障进行运动。利用基于模型的控制优势，我们的方法允许在训练或部署期间定义各种类型的硬约束和软约束，从而支持策略微调并缓解仿真到现实迁移的挑战。同时，它保留了RL在处理复杂非结构化环境中运动时的鲁棒性。引入约束后训练得到的策略被部署在一台六足机器人上，并在包括积雪斜坡和楼梯在内的多种户外环境中进行了测试，证明了我们的方法具有出色的通过性和安全性。
英文摘要：
Reinforcement learning (RL) has demonstrated impressive performance in legged locomotion over various challenging environments. However, due to the sim-to-real gap and lack of explainability, unconstrained RL policies deployed in the real world still suffer from inevitable safety issues, such as joint collisions, excessive torque, or foot slippage in low-friction environments. These problems limit its usage in missions with strict safety requirements, such as planetary exploration, nuclear facility inspection, and deep-sea operations. In this paper, we design a hierarchical optimization-based whole-body follower, which integrates both hard and soft constraints into RL framework to make the robot move with better safety guarantees. Leveraging the advantages of model-based control, our approach allows for the definition of various types of hard and soft constraints during training or deployment, which allows for policy fine-tuning and mitigates the challenges of sim-to-real transfer. Meanwhile, it preserves the robustness of RL when dealing with locomotion in complex unstructured environments. The trained policy with introduced constraints was deployed in a hexapod robot and tested in various outdoor environments, including snow-covered slopes and stairs, demonstrating the great traversability and safety of our approach.
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。
