Interaction-aware Conformal Prediction for Crowd Navigation

TL;DR

This paper presents Interaction-aware Conformal Prediction (ICP), a novel approach for crowd navigation that integrates trajectory prediction, probabilistic planning, and uncertainty quantification to improve safety and efficiency.

Contribution

The paper introduces ICP, combining conformal prediction with human trajectory modeling and MPC for improved crowd navigation under uncertainty.

Findings

ICP balances navigation efficiency, social awareness, and uncertainty quantification.

ICP generalizes well across various crowd densities.

ICP is computationally efficient for real-world applications.

Abstract

During crowd navigation, robot motion plan needs to consider human motion uncertainty, and the human motion uncertainty is dependent on the robot motion plan. We introduce Interaction-aware Conformal Prediction (ICP) to alternate uncertainty-aware robot motion planning and decision-dependent human motion uncertainty quantification. ICP is composed of a trajectory predictor to predict human trajectories, a model predictive controller to plan robot motion with confidence interval radii added for probabilistic safety, a human simulator to collect human trajectory calibration dataset conditioned on the planned robot motion, and a conformal prediction module to quantify trajectory prediction error on the decision-dependent calibration dataset. Crowd navigation simulation experiments show that ICP strikes a good balance of performance among navigation efficiency, social awareness, and uncertainty quantification compared to previous works. ICP generalizes well to navigation tasks under various crowd densities. The fast runtime and efficient memory usage make ICP practical for real-world applications. Code is available at https://github.com/tedhuang96/icp.