Back to Search Start Over

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

Authors :
Dass, Shivin
Pertsch, Karl
Zhang, Hejia
Lee, Youngwoon
Lim, Joseph J.
Nikolaidis, Stefanos
Publication Year :
2022

Abstract

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting large-scale robotic data is much more expensive and slower as each operator can control only a single robot at a time. To make this costly data collection process efficient and scalable, we propose Policy Assisted TeleOperation (PATO), a system which automates part of the demonstration collection process using a learned assistive policy. PATO autonomously executes repetitive behaviors in data collection and asks for human input only when it is uncertain about which subtask or behavior to execute. We conduct teleoperation user studies both with a real robot and a simulated robot fleet and demonstrate that our assisted teleoperation system reduces human operators' mental load while improving data collection efficiency. Further, it enables a single operator to control multiple robots in parallel, which is a first step towards scalable robotic data collection. For code and video results, see https://clvrai.com/pato<br />Comment: Robotics: Science and Systems (RSS) 2023. Website: https://clvrai.com/pato

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2212.04708
Document Type :
Working Paper