With the introduction of collaborative robots, humans and robots can now work together in close proximity and share the same workspace. However, this collaboration presents various challenges that need to be addressed to ensure seamless cooperation between the agents. This paper focuses on task planning for human-robot collaboration, taking into account the human's performance and their preference for following or leading. Unlike conventional task allocation methods, the proposed system allows both the robot and human to select and assign tasks to each other. Our previous studies evaluated the proposed framework in a computer simulation environment. This paper extends the research by implementing the algorithm in a real scenario where a human collaborates with a Fetch mobile manipulator robot. We briefly describe the experimental setup, procedure and implementation of the planned user study. As a first step, in this paper, we report on a system evaluation study where the experimenter enacted different possible behaviours in terms of leader/follower preferences that can occur in a user study. Results show that the robot can adapt and respond appropriately to different human agent behaviours, enacted by the experimenter. A future user study will evaluate the system with human participants.

翻译：随着协作机器人的引入，人类与机器人现在能够在近距离内共同工作并共享同一工作空间。然而，这种协作带来了需要解决的各种挑战，以确保代理之间的无缝配合。本文聚焦于考虑人类表现及其对引领或跟随偏好的人机协作任务规划。与传统任务分配方法不同，所提出系统允许机器人和人类相互选择并分配任务。我们先前的研究在计算机模拟环境中评估了所提框架。本文通过在实际场景中实现该算法（人类与Fetch移动操作机器人协作）扩展了研究。我们简要描述了实验设置、流程及计划中的用户研究实施。作为第一步，本文报告了系统评估研究，其中实验者模拟了用户研究中可能出现的不同引领/跟随偏好行为。结果显示，机器人能够适应并适当响应实验者模拟的不同人类代理行为。未来的用户研究将评估该系统在人类参与者中的表现。