Paper Info
Title
Scaling Up Multi-Task Robotic Reinforcement Learning.
Abstract
General-purpose robotic systems must master a large repertoire of diverse skills. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to acquire each skill makes the prospect of a generalist robot trained with RL daunting. We study how a large scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks. In this framework, new tasks can be continuously instantiated from previously learned tasks improving overall performance and capabilities of the system. To this end, we develop a scalable and intuitive framework for specifying new tasks through user-provided examples of desired outcomes, devise a multi-robot collective learning system for data collection that simultaneously collects experience for multiple tasks, and develop a scalable and generalizable multi-task deep reinforcement learning method, which we call MT-Opt. We demonstrate how MT-Opt can learn a wide range of skills, including semantic picking (i.e., picking an object from a particular category), placing into various fixtures (e.g., placing a food item onto a plate). We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots, and demonstrate the performance of our system both in terms of its ability to generalize to structurally similar new tasks, and acquire distinct new tasks more quickly by leveraging past experience. We recommend viewing the videos at https://karolhausman.github.io/mt-opt/
Year
Venue
DocType
2021
CoRL
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Dmitry Kalashnikov1252.06
Jacob Varley2265.93
Yevgen Chebotar300.34
Benjamin Swanson400.34
Rico Jonschkowski5495.88
Chelsea Finn681957.17
Sergey Levine7022.65
Karol Hausman801.69