Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation

Abstract

Reinforcement learning (RL) has shown remarkable proficiency in developing robust control policies for contact-rich applications. However, it typically requires meticulous Markov Decision Process (MDP) designing tailored to each task and robotic platform. This work addresses this challenge by creating a systematic approach to behavior synthesis and control for multi-contact loco-manipulation. We define a task-independent MDP formulation to learn robust RL policies using a single demonstration (per task) generated from a fast model-based trajectory optimization method. Our framework is validated on diverse real-world tasks, such as navigating spring-loaded doors and manipulating heavy dishwashers. The learned behaviors can handle dynamic uncertainties and external disturbances, showcasing recovery maneuvers, such as re-grasping objects during execution. Finally, we successfully transfer the policies to a real robot, demonstrating the approach's practical viability.