PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

Shivin Dass
University of Southern California
Karl Pertsch
University of Southern California
Hejia Zhang
University of Southern California
Youngwoon Lee
University of California, Berkeley
Joseph J Lim
Stefanos Nikolaidis
University of Southern California
Paper Website

Paper ID 13

Session 2. Manipulation from Demonstrations and Teleoperation

Poster Session Tuesday, July 11

Poster 13

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