用强化学习动态调整步态区域和时机，提升人形机器人抗推力能力。
Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region
- 通过强化学习动态扩展步态可落脚区域，支持越界跨步。
- 实时调整步态时间，使可恢复扰动范围扩大。
- 适合研究机器人平衡控制与自适应步态的学者参考。
本文提出一种新方法，以增强人形机器人在强外部扰动（如强烈推力）下的行走鲁棒性。有效恢复扰动依赖于双足机器人动态调整步态策略，包括落脚位置和时机。不同于多数先进行走控制器将落脚点限制在预设凸区域内、大幅限制可恢复扰动范围的做法，本文方法利用强化学习动态调整允许的落脚区域，扩展至更大、有效非凸区域，并支持越界跨步，这对应对大侧向推力至关重要。此外，该方法实时调整步态时机，进一步扩大可恢复扰动范围。基于这些调整，通过求解二次规划（QP）生成可行落脚点及动态中心质心（DCM）轨迹。最后，采用DCM控制器与逆动力学全身控制框架，确保机器人有效跟踪轨迹。
原文摘要 · Abstract (English)
This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory.
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