PATO: ポリシー支援型遠隔操作によるスケーラブルなロボットデータ収集
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
学習した支援ポリシーで遠隔操作の一部を自動化し、人間は不確実なときだけ介入する仕組みを提案。実機とシミュレーションで作業負担を減らしつつデータ収集効率を向上させ、1人で複数ロボットを並行操作できることを示した。
著者: Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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
