A Study on Learning Social Robot Navigation with Multimodal Perception

TL;DR

This paper investigates how multimodal perception improves social robot navigation by comparing learning approaches and analyzing social compliance, demonstrating significant advantages over unimodal methods through real-world data and human studies.

Contribution

It provides a comprehensive analysis of multimodal perception in social robot navigation, including a large-scale dataset, comparative evaluation, and insights into social compliance.

Findings

Multimodal learning outperforms unimodal in navigation accuracy.

Multimodal perception enhances perceived social compliance.

Open-source code facilitates future research in this area.

Abstract

Autonomous mobile robots need to perceive the environments with their onboard sensors (e.g., LiDARs and RGB cameras) and then make appropriate navigation decisions. In order to navigate human-inhabited public spaces, such a navigation task becomes more than only obstacle avoidance, but also requires considering surrounding humans and their intentions to somewhat change the navigation behavior in response to the underlying social norms, i.e., being socially compliant. Machine learning methods are shown to be effective in capturing those complex and subtle social interactions in a data-driven manner, without explicitly hand-crafting simplified models or cost functions. Considering multiple available sensor modalities and the efficiency of learning methods, this paper presents a comprehensive study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. The study investigates social robot navigation decision making on both the global and local planning levels and contrasts unimodal and multimodal learning against a set of classical navigation approaches in different social scenarios, while also analyzing the training and generalizability performance from the learning perspective. We also conduct a human study on how learning with multimodal perception affects the perceived social compliance. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies. We open-source our code for the community's future use to study multimodal perception for learning social robot navigation.