面向鲁棒多接触运动操作的引导式强化学习
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
AI总结：
本文提出任务无关MDP与自适应相位动力学引导的强化学习方法，仅需单次演示即可实现多接触运动操作，并在真实机器人上验证了鲁棒性与可行性。
摘要：
强化学习（RL）通常需要为每个任务精心设计马尔可夫决策过程（MDP）。本研究旨在通过提出一种系统化的行为合成与控制方法来应对这一挑战，该方法适用于多接触运动操作任务，例如推开弹簧门和操纵重型洗碗机。我们定义了一个任务无关的MDP，仅使用每个任务由基于模型的轨迹优化器生成的一次演示来训练RL策略。我们的方法结合了自适应相位动力学公式，以鲁棒地跟踪演示，同时适应动态不确定性和外部扰动。我们将我们的方法与先前的运动模仿RL工作进行了比较，结果表明所学策略在所有考虑的任务中均取得了更高的成功率。这些策略学会了演示中不存在的恢复动作，例如在执行过程中重新抓取物体或处理打滑情况。最后，我们成功地将策略迁移到真实机器人上，证明了我们方法的实际可行性。
英文摘要：
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a systematic approach to behavior synthesis and control for multi-contact loco-manipulation tasks, such as navigating spring-loaded doors and manipulating heavy dishwashers. We define a task-independent MDP to train RL policies using only a single demonstration per task generated from a model-based trajectory optimizer. Our approach incorporates an adaptive phase dynamics formulation to robustly track the demonstrations while accommodating dynamic uncertainties and external disturbances. We compare our method against prior motion imitation RL works and show that the learned policies achieve higher success rates across all considered tasks. These policies learn recovery maneuvers that are not present in the demonstration, such as re-grasping objects during execution or dealing with slippages. Finally, we successfully transfer the policies to a real robot, demonstrating the practical viability of our 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。
