Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments

TL;DR

This paper introduces a novel social motion latent space for robot navigation that improves success rate, efficiency, and smoothness in crowded environments, while also incorporating human awareness to enhance interaction and trajectory quality.

Contribution

It presents a new social motion latent space for robot control and integrates human awareness into navigation, advancing social robot navigation capabilities.

Findings

Significant improvements in success rate and navigation efficiency.

Smoother and more anticipatory robot trajectories.

Incorporation of human awareness enhances interaction and trajectory quality.

Abstract

This work proposes a novel approach to social robot navigation by learning to generate robot controls from a social motion latent space. By leveraging this social motion latent space, the proposed method achieves significant improvements in social navigation metrics such as success rate, navigation time, and trajectory length while producing smoother (less jerk and angular deviations) and more anticipatory trajectories. The superiority of the proposed method is demonstrated through comparison with baseline models in various scenarios. Additionally, the concept of humans' awareness towards the robot is introduced into the social robot navigation framework, showing that incorporating human awareness leads to shorter and smoother trajectories owing to humans' ability to positively interact with the robot.