强化学习增强的MPC用于非步态腿部和混合运动
RL-Augmented MPC for Non-Gaited Legged and Hybrid Locomotion
AI总结：
本文提出一种结合强化学习和模型预测控制的分层架构，通过模拟中学习无环步态实现接触时间的卸载，验证了在不同机器人平台上的有效性及非平坦地形的扩展性。
摘要：
我们提出了一种接触显式分层架构，将强化学习（RL）与模型预测控制（MPC）相结合，其中高级RL代理为低级运动MPC提供步态和导航命令。通过模拟中的试错学习无环步态，将接触时间的组合负担卸载至MPC。我们证明仅需最小奖励集和有限调参即可获得有效策略。在50kg至120kg的机器人平台及不同MPC实现上验证了该架构，观察到在平坦地形腿部和混合运动中的无环步态和时间适应，进一步展示了非平坦地形的扩展性。所有平台均实现了无领域随机化的sim-to-sim迁移，并在Centauro 120kg轮式人形机器人上实现了无领域随机化的sim-to-real迁移。我们的软件框架和评估结果已公开在https://github.com/AndrePatri/AugMPC。
英文摘要：
We propose a contact-explicit hierarchical architecture coupling Reinforcement Learning (RL) and Model Predictive Control (MPC), where a high-level RL agent provides gait and navigation commands to a low-level locomotion MPC. This offloads the combinatorial burden of contact timing from the MPC by learning acyclic gaits through trial and error in simulation. We show that only a minimal set of rewards and limited tuning are required to obtain effective policies. We validate the architecture in simulation across robotic platforms spanning 50 kg to 120 kg and different MPC implementations, observing the emergence of acyclic gaits and timing adaptations in flat-terrain legged and hybrid locomotion, and further demonstrating extensibility to non-flat terrains. Across all platforms, we achieve zero-shot sim-to-sim transfer without domain randomization, and we further demonstrate zero-shot sim-to-real transfer without domain randomization on Centauro, our 120 kg wheeled-legged humanoid robot. We make our software framework and evaluation results publicly available at https://github.com/AndrePatri/AugMPC.
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。
