Group-based Motion Prediction for Navigation in Crowded Environments

TL;DR

This paper introduces G-MPC, a group-based motion prediction framework inspired by Gestalt theory, improving robot navigation safety and social compliance in crowded environments by considering group dynamics.

Contribution

The paper presents a novel group-based prediction model for robot motion planning that outperforms individual-based models in crowded scenes.

Findings

G-MPC achieves higher safety in navigation tasks.

G-MPC results in fewer group intrusions.

G-MPC handles noisy sensor data effectively.

Abstract

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.