When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks. However, less emphasis has been placed on the effect of the environment on coordination behaviors. To thoroughly explore environments that result in diverse behaviors, we propose a framework for procedural generation of environments that are (1) stylistically similar to human-authored environments, (2) guaranteed to be solvable by the human-robot team, and (3) diverse with respect to coordination measures. We analyze the procedurally generated environments in the Overcooked benchmark domain via simulation and an online user study. Results show that the environments result in qualitatively different emerging behaviors and statistically significant differences in collaborative fluency metrics, even when the robot runs the same planning algorithm.

翻译：在研究与人类合作的机器人时,大部分重点一直放在在协作任务中与人类团队协调的机器人政策上。然而,对于环境对协调行为的影响重视不够。为了彻底探索导致不同行为的环境,我们提出了一个程序环境生成框架,即(1) 与人造环境类似,(2) 人类机器人团队保证可以溶解,(3) 协调措施方面的多样性。我们通过模拟和在线用户研究分析过大基准域中程序生成的环境。结果显示,环境导致质量上不同的新行为和在统计上显著的差异,即使机器人运行同样的规划算法。