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 inevitable 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

翻译：辅助机器人手臂旨在帮助用户完成日常任务。机器人提供这种帮助的一种方式是共享自主。在共享自主中，人和机器人共同控制机器人的运动：当机器人确信理解人类意图时，它会介入以自动化任务。但机器人最初如何获知这些任务？现有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了解人类的潜在目标。在长期交互中，这些方法难免会失效——人类迟早会尝试执行机器人未预期的任务。因此，本文提出一种从零开始学习辅助能力的共享自主替代方案。我们的核心洞察是：操作者每天重复执行重要任务（如打开冰箱、煮咖啡）。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。我们提出SARI算法，它能识别人类任务、复现相似演示，并在不确定时交还控制权。随后，我们将学习与控制相结合，证明本方法的误差是一致最终有界的。我们通过仿真验证该误差界，将本方法与模仿学习基线对比，并探索其对递增任务数量的辅助能力。最后，我们采用行业标准方法和共享自主基线开展了三项用户研究，包括一项针对残疾用户的试点测试。结果表明，跨重复交互的共享自主学习在已知任务上匹配现有方法，且在新任务上优于基线。用户研究视频见：https://youtu.be/3vE4omSvLvc