分离步态与支撑腿控制，实现机器人动态自适应行走。
Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control
- 将步态控制与支撑腿受力控制解耦，提升适应性。
- 在不平地形和负载偏移下仍保持稳定行走性能。
- 适合需要快速应对复杂环境的机器人系统研究者。
强化学习在足式机器人行走控制中取得显著进展，但在分布外（OOD）条件和仿真到现实的差异下性能下降。本文提出一种新型解耦框架，通过分离支撑腿控制与摆动腿控制，实现快速在线自适应，有效缓解仿真与真实环境间的差距，提升在未知环境中的鲁棒性。多种仿真和真实世界实验验证了其在水平力扰动、不平地形、重载与偏载情况下的有效性，显著改善了模拟到现实的迁移性能。
原文摘要 · Abstract (English)
While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditions and discrepancies between the simulation and the real environments. Instead of mainly relying on domain randomization (DR) to best cover the real environments and thereby close the sim-to-real gap and enhance robustness, this work proposes an emerging decoupled framework that acquires fast online adaptation ability and mitigates the sim-to-real problems in unfamiliar environments by isolating stance-leg control and swing-leg control. Various simulation and real-world experiments demonstrate its effectiveness against horizontal force disturbances, uneven terrains, heavy and biased payloads, and sim-to-real gap.
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