PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
This addresses the problem of scalable robot data collection for robotics and machine learning researchers, representing an incremental step towards more efficient data gathering.
The paper tackles the high cost and inefficiency of collecting large-scale robotic data by proposing PATO, a system that uses a learned assistive policy to automate repetitive tasks and request human input only when uncertain, enabling a single operator to control multiple robots in parallel.
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