  A widely accepted explanation for robots planning overcautious or
overaggressive trajectories alongside human is that the crowd density exceeds a
threshold such that all feasible trajectories are considered unsafe -- the
freezing robot problem. However, even with low crowd density, the robot's
navigation performance could still drop drastically when in close proximity to
human. In this work, we argue that a broader cause of suboptimal navigation
performance near human is due to the robot's misjudgement for the human's
willingness (flexibility) to share space with others, particularly when the
robot assumes the human's flexibility holds constant during interaction, a
phenomenon of what we call human robot pacing mismatch. We show that the
necessary condition for solving pacing mismatch is to model the evolution of
both the robot and the human's flexibility during decision making, a strategy
called distribution space modeling. We demonstrate the advantage of
distribution space coupling through an anecdotal case study and discuss the
future directions of solving human robot pacing mismatch.
