ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors
Year: 2021
Type: preprint
Abstract: In this paper, we study how robots can autonomously learn skills that requirea 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.
Source: arXiv (Cornell University)
Authors: Charles Sun, Jędrzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3186884957