模仿与微调模型预测控制实现鲁棒且对称的四足运动
Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion
AI总结：
提出模仿与微调模型预测控制（IFM）框架，结合模型预测控制与模仿学习及强化学习，提升四足机器人在复杂地形上的运动性能、对称性和能效。
摘要：
腿式机器人的控制是一个具有挑战性的问题，已有多种方法进行研究，如基于模型的控制和学习算法。本文提出了一种新颖的模仿与微调模型预测控制（IFM）框架，以结合两种方法的优势。该框架首先使用微分动态规划和Raibert启发式方法开发一个传统的模型预测控制器（MPC），作为专家策略。然后，通过模仿学习训练MPC的克隆，使控制器可学习。最后，利用有限探索的深度强化学习在更具挑战性的地形上进一步微调策略。通过全面的仿真和硬件实验，我们证明了所提出的IFM框架能够显著提高给定MPC控制器在粗糙、湿滑和传送带等需要仔细协调步态的地形上的性能。我们还展示了与普通强化学习相比，IFM能够以最小的奖励塑造负担高效地产生更对称、周期性和节能的步态。
英文摘要：
Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algorithms. This work proposes a novel Imitating and Finetuning Model Predictive Control (IFM) framework to take the strengths of both approaches. Our framework first develops a conventional model predictive controller (MPC) using Differential Dynamic Programming and Raibert heuristic, which serves as an expert policy. Then we train a clone of the MPC using imitation learning to make the controller learnable. Finally, we leverage deep reinforcement learning with limited exploration for further finetuning the policy on more challenging terrains. By conducting comprehensive simulation and hardware experiments, we demonstrate that the proposed IFM framework can significantly improve the performance of the given MPC controller on rough, slippery, and conveyor terrains that require careful coordination of footsteps. We also showcase that IFM can efficiently produce more symmetric, periodic, and energy-efficient gaits compared to Vanilla RL with a minimal burden of reward shaping.
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。
