面向高效机器人操作技能获取的人机联合学习
Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition
AI总结：
本文提出人机联合学习系统，让人类操作员与学习型辅助智能体共享机器人末端控制权，在数据积累中逐步减少人工干预，从而提升演示收集效率并保证下游任务数据质量。
摘要：
采用遥操作系统收集演示数据，为更高效地学习机器人操作提供了可能。然而，通过遥操作系统操控配备灵巧手或夹爪的机械臂，由于任务的高维度、运动复杂性以及生理结构差异，存在固有挑战。在本研究中，我们提出了一种新颖的人机联合学习系统，使人类操作员能够与学习型辅助智能体共享对机器人末端执行器的控制，简化数据收集过程，并促进人类演示收集与机器人操作训练的同步进行。随着数据的积累，辅助智能体逐步学习。因此，所需的人类精力和注意力减少，从而提高了数据收集过程的效率。该系统还允许人类操作员调整控制比例，以在手动控制与自动控制之间取得权衡。我们在模拟环境和真实物理场景中进行了实验。通过用户研究和定量评估，结果表明所提出的系统能够提高数据收集效率，减少人类适应需求，同时确保收集的数据对下游任务具有足够的质量。更多详情，请参阅我们的网页 https://norweig1an.github.io/HAJL.github.io/。
英文摘要：
Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system presents inherent challenges due to the task's high dimensionality, complexity of motion, and differences between physiological structures. In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, simplifies the data collection process, and facilitates simultaneous human demonstration collection and robot manipulation training. As data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control. We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks. \textit{For more details, please refer to our webpage https://norweig1an.github.io/HAJL.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
