  We focus on the problem of planning the motion of a robot in a dynamic
multiagent environment such as a pedestrian scene. Enabling the robot to
navigate safely and in a socially compliant fashion in such scenes requires a
representation that accounts for the unfolding multiagent dynamics. Existing
approaches to this problem tend to employ microscopic models of motion
prediction that reason about the individual behavior of other agents. While
such models may achieve high tracking accuracy in trajectory prediction
benchmarks, they often lack an understanding of the group structures unfolding
in crowded scenes. Inspired by the Gestalt theory from psychology, we build a
Model Predictive Control framework (G-MPC) that leverages group-based
prediction for robot motion planning. We conduct an extensive simulation study
involving a series of challenging navigation tasks in scenes extracted from two
real-world pedestrian datasets. We illustrate that G-MPC enables a robot to
achieve statistically significantly higher safety and lower number of group
intrusions than a series of baselines featuring individual pedestrian motion
prediction models. Finally, we show that G-MPC can handle noisy lidar-scan
estimates without significant performance losses.
