Task Autocorrection for Immersive Teleoperation
Task Autocorrection for Immersive Teleoperation
复制标题
沉浸式远程操作的任务自动校正
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Roi Poranne
中科院分区：
文献类型：
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作者：
Chenyang Wang;Simon Huber;Stelian Coros;Roi Poranne
远程操作机器人手臂是一项具有挑战性的任务，需要多年的训练才能掌握。它对脑力要求很高，因为操作员必须在内部计算变换，或者依赖肌肉记忆来执行即使是最简单的任务。依赖于具体化的替代方法-从机器人的角度控制机器人的沉浸式第一人称体验最近变得越来越流行，这要归功于混合现实设备的出现。这些方法通过跟踪用户的运动并将其重新传送到机器人来创建直观的体验。然而，由于固有的差异，如延迟，不完美的跟踪，以及人类和机器人电机系统之间的差异，即使是最近的硬件也无法实现完全沉浸。因此，即使是简单的拾取和放置任务，这些系统，虽然更直观，仍然是繁琐的，远离人类performance.In本文中，我们提出了一个沉浸式系统，旨在弥合这一差距的水平。该系统跟踪用户的动作，并像往常一样将它们重新定位到机器人，但它也检测用户的意图，即他们希望执行的任务。基于此知识，系统可以在运动即将失败时以无缝方式自动校正运动，使得任务成功执行。我们在用户研究中评估我们的自动校正系统的功效。结果显示，在操作精度和时间方面，性能有了统计上的显著提高。
Teleoperating robotic arms is a challenging task that requires years of training to master. It is mentally demanding, as the operator must internally compute transformations, or rely on muscle memory, to perform even the simplest tasks. Alternative methods that rely on embodiment –the immersive, first person experience of controlling the robot from its point of view are recently becoming more popular, thanks to the emergence of mixed reality devices. These methods create an intuitive experience by tracking the users motions, and retargetting them to the robot. However, even recent hardware fails at achieving total immersion, due to inherent discrepancies such as latency, imperfect tracking, and the differences between human and robot motor systems. Thus, performing even simple pick-and-place tasks with these systems, while more intuitive, is still cumbersome, and far from the level of human performance.In this paper we propose an immersive system that aims to bridge this gap. The system tracks the user’s motion and retargets them to the robot as usual, but it also detects the user’s intent, that is, the task they wish to perform. Based on this knowledge, the system can autocorrect the motion when it is about to fail, in a seamless manner, such that the task is successfully performed. We evaluate the efficacy of our autocorrection system in a user study. The results show a statistically significant performance improvement in terms of operation accuracy and time.