ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors

In this paper, we study how mobile manipulators can autonomously learn skills that require a combination of navigation and grasping. Learning robotic skills in the real world remains challenging without large scale data collection and supervision. These difficulties have often been sidestepped by limiting the robot to only manipulation or navigation, and by using human effort to provide demonstrations, task resets, and data labeling during the training process. Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in a way that minimizes human intervention and enables continual learning under realistic assumptions. Specifically, our system, ReLMM, can learn continuously on a real-world platform without any environment instrumentation, with minimal human intervention, and without access to privileged information, such as maps, objects positions, or a global view of the environment. Our method employs a modularized policy with components for manipulation and navigation, where uncertainty over the manipulation value function drives exploration for the navigation controller, and the success of the manipulation module provides rewards for navigation. We evaluate our method on a room cleanup task, where the robot must pick up each item of clutter from the floor. After a brief grasp pretraining phase with human oversight, ReLMM can learn navigation and grasping together fully automatically, in around 40 hours of real-world training with minimal human intervention.