论文标题
PATO：可扩展机器人数据收集的政策协助索引
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
论文作者
论文摘要
大规模数据是机器学习的重要组成部分，如自然语言处理和计算机视觉研究的最新进展所示。但是，收集大规模机器人数据要昂贵得多，速度较慢，因为每个操作员一次只能控制一个机器人。为了使这一昂贵的数据收集过程有效且可扩展，我们建议使用学识渊博的辅助政策自动化示范收集过程的一部分系统。 PATO自主在数据收集中执行重复行为，并且只有在不确定要执行哪种子任务或行为的情况下才要求人类输入。我们使用真正的机器人和模拟机器人机队进行远程运输用户研究，并证明我们的辅助远程操作系统会减少人类操作员的精神负载，同时提高数据收集效率。此外，它使单个操作员能够并行控制多个机器人，这是迈向可扩展机器人数据收集的第一步。有关代码和视频结果，请参见https://clvrai.com/pato
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
