  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.
