穿越狭窄路径：一种用于鲁棒且安全的人形机器人行走的两阶段强化学习框架
Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal
AI总结：
本文提出一种两阶段强化学习框架，结合模板落脚点规划器、低层跟踪器与轻量级感知修正器，通过课程训练实现人形机器人在狭窄路径上的鲁棒安全行走，并在 Unitree G1 上完成 20 次无失败穿越验证。
摘要：
由于所需落脚点稀疏且对安全性要求极高，穿越狭窄路径对人形机器人而言具有挑战性。纯基于模板或基于端到端强化学习的方法在此类严苛地形上表现不佳。本文针对此类狭窄路径穿越任务提出了一种两阶段训练框架，将基于模板的落脚点规划器与来自第一阶段训练的低层落脚点跟踪器以及来自第二阶段训练的轻量级感知辅助落脚点修正器相结合。通过在阶段间设置从平坦地面到狭窄路径的课程，最终得到的控制器进而学会鲁棒地跟踪并安全地修正落脚点目标，以确保在狭窄路径上实现精确的足部放置。该框架保留了基于物理模板的可解释性，并利用了强化学习的泛化能力，从而易于实现从仿真到现实的迁移。所学习到的策略在成功率、中心线保持度和安全裕度方面均优于纯基于模板或基于强化学习的基线方法。在 Unitree G1 人形机器人上的验证结果表明，其成功穿越一条宽 0.2 米、长 3 米的横梁，进行了 20 次试验且无任何失败。
英文摘要：
Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow path traversing tasks, coupling a template-based foothold planner with a low-level foothold tracker from Stage-I training and a lightweight perception aided foothold modifier from Stage-II training. With the curriculum setup from flat ground to narrow paths across stages, the resulted controller in turn learns to robustly track and safely modify foothold targets to ensure precise foot placement over narrow paths. This framework preserves the interpretability from the physics-based template and takes advantage of the generalization capability from reinforcement learning, resulting in easy sim-to-real transfer. The learned policies outperform purely template-based or reinforcement learning-based baselines in terms of success rate, centerline adherence and safety margins. Validation on a Unitree G1 humanoid robot yields successful traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.
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。
