Understanding and Imitating Human-Robot Motion with Restricted Visual Fields

TL;DR

This paper demonstrates that modeling agents' limited perception capabilities, such as restricted visual fields, improves the prediction of their behavior and enhances robot navigation in cluttered environments, including real-time physical navigation.

Contribution

It introduces a perception-aware approach that models agents' limited visual fields independently from their motion policies, improving behavior prediction and navigation.

Findings

Reasoning about perception improves behavior prediction.

Perception-aware models enhance robot navigation accuracy.

The approach works in real-time on physical hardware.

Abstract

When working around other agents such as humans, it is important to model their perception capabilities to predict and make sense of their behavior. In this work, we consider agents whose perception capabilities are determined by their limited field of view, viewing range, and the potential to miss objects within their viewing range. By considering the perception capabilities and observation model of agents independently from their motion policy, we show that we can better predict the agents' behavior; i.e., by reasoning about the perception capabilities of other agents, one can better make sense of their actions. We perform a user study where human operators navigate a cluttered scene while scanning the region for obstacles with a limited field of view and range. We show that by reasoning about the limited observation space of humans, a robot can better learn a human's strategy for navigating an environment and navigate with minimal collision with dynamic and static obstacles. We also show that this learned model helps it successfully navigate a physical hardware vehicle in real-time. Code available at https://github.com/labicon/HRMotion-RestrictedView.