Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily focuses on one human sharing autonomy with a single robot. By contrast, in this paper we present an approach for multi-robot shared autonomy that enables one operator to provide real-time corrections across two coordinated robots completing the same task in parallel. Sharing autonomy with multiple robots presents fundamental challenges. The human can only correct one robot at a time, and without coordination, the human may be left idle for long periods of time. Accordingly, we develop an approach that aligns the robot's learned motions to best utilize the human's expertise. Our key idea is to leverage Learning from Demonstration (LfD) and time warping to schedule the motions of the robots based on when they may require assistance. Our method uses variability in operator demonstrations to identify the types of corrections an operator might apply during shared autonomy, leverages flexibility in how quickly the task was performed in demonstrations to aid in scheduling, and iteratively estimates the likelihood of when corrections may be needed to ensure that only one robot is completing an action requiring assistance. Through a preliminary study, we show that our method can decrease the scheduled time spent sanding by iteratively estimating the times when each robot could need assistance and generating an optimized schedule that allows the operator to provide corrections to each robot during these times.

翻译：共享自主方法通过人类操作员与机器人臂的协同工作，使机器人能够完成一系列复杂且高度可变的任务。现有研究主要聚焦于单个人类与单个机器人之间的自主共享。相比之下，本文提出一种面向多机器人共享自主的方法，使得一名操作员能够对并行完成同一任务的两台协调机器人进行实时校正。与多台机器人共享自主面临根本性挑战：人类一次只能校正一台机器人，若缺乏协调，操作员可能长期处于空闲状态。为此，我们开发了一种方法，通过对齐机器人习得的动作来最优利用人类专长。核心思想是利用示教学习（LfD）与时间扭曲技术，根据机器人可能需要的辅助时机来调度其运动。该方法通过操作员示教中的变异性识别共享自主期间可能应用的校正类型，利用示教中任务执行速度的灵活性优化调度，并迭代估计可能需要校正的时刻，确保仅有一台机器人执行需要辅助的动作。初步研究表明，通过迭代估计每台机器人可能需要的辅助时机并生成优化调度方案，本方法可减少安排好的打磨任务总执行时间，使操作员能在这些时刻对每台机器人提供校正。