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Large-scale data is an essential component of machine learning asdemonstrated in recent advances in natural language processing and computervision research. However, collecting large-scale robotic data is much moreexpensive and slower as each operator can control only a single robot at atime. To make this costly data collection process efficient and scalable, wepropose Policy Assisted TeleOperation (PATO), a system which automates part ofthe demonstration collection process using a learned assistive policy. PATOautonomously executes repetitive behaviors in data collection and asks forhuman input only when it is uncertain about which subtask or behavior toexecute. We conduct teleoperation user studies both with a real robot and asimulated robot fleet and demonstrate that our assisted teleoperation systemreduces human operators' mental load while improving data collectionefficiency. Further, it enables a single operator to control multiple robots inparallel, which is a first step towards scalable robotic data collection. Forcode and video results, see https://clvrai.com/pato

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection