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

翻译：大规模数据是机器学习的重要组成部分，这一点在自然语言处理和计算机视觉研究的最新进展中已得到充分证明。然而，大规模机器人数据的采集成本更高、速度更慢，因为每位操作员一次只能控制一台机器人。为了提升这一高成本数据采集过程的效率和可扩展性，我们提出了一种名为策略辅助遥操作（PATO）的系统，该系统利用学习到的辅助策略自动完成部分演示采集过程。PATO在数据采集中自主执行重复性行为，仅在不确定要执行哪个子任务或行为时请求人工输入。我们针对真实机器人和模拟机器人集群开展了遥操作用户研究，结果表明，我们的辅助遥操作系统在提升数据采集效率的同时，减轻了人类操作员的认知负荷。此外，该系统使得单一操作员能够并行控制多台机器人，这为可扩展的机器人数据采集迈出了第一步。有关代码和视频结果，请参见https://clvrai.com/pato。