Robotics: Science and Systems XIX

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J Lim, Stefanos Nikolaidis


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



    AUTHOR    = {Shivin Dass AND Karl Pertsch AND Hejia Zhang AND Youngwoon Lee AND Joseph J Lim AND Stefanos Nikolaidis}, 
    TITLE     = {{PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.013}