Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans' estimate of a robot's reachable workspace through a small number of demonstrations before collaboration. We develop a novel motion planning method, REMP (Reachability-Expressive Motion Planning), which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot's motion capability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task.
翻译:人类对机器人真正能力所能做的评估对在人类和机器人伙伴合作执行共同任务时建立共同点至关重要。 在这项工作中,我们提出一种方法,通过协作前的少量演示校准人类对机器人可到达的工作空间的估计。我们开发了一种新型的动作规划方法,即ReMP(Recable-Expressivemotion Plan),它共同优化了机器人运动的物理成本和表达能力,以便向人类观察员披露机器人的动作能力。我们与人类参与者的实验表明,使用REMP的短期校准可以有效地弥合非专家用户认为机器人能够达到的程度与地面真实性之间的差距。我们表明,这种校准程序不仅能提高用户的认识,而且还能促进在随后的共同任务中进行更有效的人-机器人协作。