用于复杂地形双足运动的强化学习增强型自适应模型预测控制
RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain
AI总结：
针对复杂地形双足运动中MPC地形建模难、RL无约束保证的问题，提出RL增强型MPC框架，参数化MPC关键组件，仿真验证其适应性和鲁棒性优于基线MPC与RL。
摘要：
模型预测控制（MPC）已在仿人双足运动中展现出有效性；然而，其在崎岖、湿滑等复杂环境中的适用性受限于地形交互建模的难度。相比之下，强化学习（RL）在多样地形下训练鲁棒运动策略方面取得了显著成功，但缺乏约束满足的保证，且常需大量奖励塑形。近期结合MPC与RL的尝试显示出融合两者优势的潜力，但主要局限于平坦地形或四足机器人。本研究提出一种针对崎岖、湿滑地形双足运动的RL增强型MPC框架，对基于单刚体动力学的MPC的三个关键组件（系统动力学、摆动腿控制器、步态频率）进行参数化。通过在NVIDIA IsaacLab中针对楼梯、踏脚石、低摩擦表面等多种地形的双足机器人仿真验证，实验结果表明，与基线MPC和RL相比，该RL增强型MPC框架产生的行为具有显著更高的适应性和鲁棒性。
英文摘要：
Model predictive control (MPC) has demonstrated effectiveness for humanoid bipedal locomotion; however, its applicability in challenging environments, such as rough and slippery terrain, is limited by the difficulty of modeling terrain interactions. In contrast, reinforcement learning (RL) has achieved notable success in training robust locomotion policies over diverse terrain, yet it lacks guarantees of constraint satisfaction and often requires substantial reward shaping. Recent efforts in combining MPC and RL have shown promise of taking the best of both worlds, but they are primarily restricted to flat terrain or quadrupedal robots. In this work, we propose an RL-augmented MPC framework tailored for bipedal locomotion over rough and slippery terrain. Our method parametrizes three key components of single-rigid-body-dynamics-based MPC: system dynamics, swing leg controller, and gait frequency. We validate our approach through bipedal robot simulations in NVIDIA IsaacLab across various terrains, including stairs, stepping stones, and low-friction surfaces. Experimental results demonstrate that our RL-augmented MPC framework produces significantly more adaptive and robust behaviors compared to baseline MPC and RL.
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