SARI：重复交互中的共享自主
SARI: Shared Autonomy across Repeated Interaction
AI总结：
本文提出SARI算法，通过利用重复交互从头学习辅助策略，在不确定时返回控制权，实现对已知任务匹配现有方法、对新任务优于基线的共享自主。
摘要：
辅助机械臂试图帮助其用户执行日常任务。机器人提供这种辅助的一种方式是共享自主。在共享自主中，人类和机器人共同保持对机器人运动的的控制：随着机器人确信自己理解了人类的意图，它便会介入以实现任务自动化。但机器人最初是如何知道这些任务的呢？最先进的共享自主方法通常依赖先验知识。例如，机器人可能需要事先知道人类的潜在目标。在长期交互中，这些方法不可避免地会失效——迟早有一天，人类会试图执行机器人未预料到的任务。因此，在本文中，我们提出了一种从头学习辅助的替代共享自主方法。我们的洞察是操作者每天重复重要的任务（例如，打开冰箱、制作咖啡）。因此，我们不依赖先验知识，而是利用这些重复交互来学习辅助策略。我们引入了SARI，一种识别人类任务、复制相似演示并在不确定时返回控制权的算法。然后，我们将学习与控制相结合，证明我们方法的误差是均匀最终有界的。我们进行仿真以支持这一误差界限，将我们的方法与模仿学习基线进行比较，并探索其对日益增多任务的辅助能力。最后，我们采用行业标准和共享自主基线进行了三项用户研究，包括一项残疾人用户的试点测试。结果表明，跨重复交互学习共享自主在已知任务上与现有方法相当，在新任务上优于基线。请在此处观看我们的用户研究视频：https://youtu.be/3vE4omSvLvc
英文摘要：
Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot's motion: as the robot becomes confident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the first place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human's potential goals beforehand. During long-term interaction these methods will inevitably break down -- sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this paper we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making coffee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human's task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc
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。
