用于敏捷与鲁棒双足步态运动规划与控制的RL增强MPC框架
RL-augmented MPC Framework for Agile and Robust Bipedal Footstep Locomotion Planning and Control
AI总结：
本文提出结合MPC与RL的双足落脚点规划控制框架，利用学习策略弥合简化模型与全阶系统差距，在DRACO 3机器人上验证了动态运动性能的显著提升。
摘要：
本文提出了一种结合模型预测控制（MPC）和强化学习（RL）的在线双足落脚点规划策略，以实现敏捷且鲁棒的双足运动。尽管基于MPC的落脚点控制器在实现动态运动方面已证明其有效性，但其性能通常因简化模型和假设的使用而受到限制。为了解决这一挑战，我们开发了一种新颖的落脚点控制器，该控制器利用学习到的策略来弥合简化模型的使用与更复杂的全阶机器人系统之间的差距。具体而言，我们的方法采用独特的组合，即基于ALIP的MPC落脚点控制器用于次优落脚点规划，以及学习到的策略用于细化落脚点调整，使得生成的落脚点策略能够有效捕捉机器人的全身动力学。这种整合将MPC的预测能力与RL的灵活性和适应性协同起来。我们通过使用全身人形机器人DRACO 3进行的一系列实验验证了框架的有效性。结果表明，动态运动性能有显著提升，包括更好地跟踪各种行走速度，实现可靠的转向和穿越具有挑战性的地形，同时与基线ALIP-based MPC方法相比，保持了行走步态的鲁棒性和稳定性。
英文摘要：
This paper proposes an online bipedal footstep planning strategy that combines model predictive control (MPC) and reinforcement learning (RL) to achieve agile and robust bipedal maneuvers. While MPC-based foot placement controllers have demonstrated their effectiveness in achieving dynamic locomotion, their performance is often limited by the use of simplified models and assumptions. To address this challenge, we develop a novel foot placement controller that leverages a learned policy to bridge the gap between the use of a simplified model and the more complex full-order robot system. Specifically, our approach employs a unique combination of an ALIP-based MPC foot placement controller for sub-optimal footstep planning and the learned policy for refining footstep adjustments, enabling the resulting footstep policy to capture the robot's whole-body dynamics effectively. This integration synergizes the predictive capability of MPC with the flexibility and adaptability of RL. We validate the effectiveness of our framework through a series of experiments using the full-body humanoid robot DRACO 3. The results demonstrate significant improvements in dynamic locomotion performance, including better tracking of a wide range of walking speeds, enabling reliable turning and traversing challenging terrains while preserving the robustness and stability of the walking gaits compared to the baseline ALIP-based MPC approach.
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。
