A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

TL;DR

This paper introduces a Graph Neural Network-based model to predict human disruption during robot navigation, improving social compliance and scalability compared to traditional handcrafted models, validated on an enhanced dataset.

Contribution

It presents a novel GNN model for social navigation disruption prediction and an improved dataset with scenario-to-graph transformation, enabling better scalability and performance.

Findings

Model achieves near-human accuracy on the dataset

Scalable approach considers multiple social factors

Enhanced dataset and transformation method provided

Abstract

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people's paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 [25] which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository (https://github.com/gnns4hri/sngnnv2).