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 simulated study, we show that our method can decrease the overall 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。现有研究主要集中于人类与单台机器人之间的自主共享。与此不同，本文提出了一种多机器人共享自主方法，使操作员能够对并行执行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：人类每次只能校正一台机器人，且缺乏协调的情况下，操作员可能长时间处于空闲状态。为此，我们开发了一种方法，使机器人习得的运动模式与人类专长实现最优配合。核心思想是利用示教学习（LfD）与时间规整技术，根据机器人可能需要协助的时机来调度其运动序列。该方法通过操作员示范的变异性识别共享自主过程中可能实施的校正类型，利用示范中任务执行速度的灵活性辅助调度，并迭代估计校正需求的概率，确保只有一台机器人执行需要协助的动作。通过初步仿真实验，我们证明该方法能通过迭代估算各机器人需要协助的时间点，生成优化调度方案，使操作员能在这些时段对每台机器人进行校正，从而减少整体打磨作业时间。