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

• URL: http://arxiv.org/abs/2107.13545v1
• Date: Wed, 28 Jul 2021 17:59:41 GMT
• Title: ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors
• Authors: Charles Sun, J\k{e}drzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine
• Abstract summary: We study how robots can autonomously learn skills that require a combination of navigation and grasping. Our system, ReLMM, can learn continuously on a real-world platform without any environment instrumentation. After a grasp curriculum training phase, ReLMM can learn navigation and grasping together fully automatically, in around 40 hours of real-world training.
• Score: 64.2809875343854
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we study how robots 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. Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an \textit{autonomous} way without human intervention, enabling continual learning under realistic assumptions. Specifically, our system, ReLMM, can learn continuously on a real-world platform without any environment instrumentation, without 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 success drives exploration for the navigation controller, and the manipulation module provides rewards for navigation. We evaluate our method on a room cleanup task, where the robot must navigate to and pick up items of scattered on the floor. After a grasp curriculum training phase, ReLMM can learn navigation and grasping together fully automatically, in around 40 hours of real-world training.

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