neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction

TL;DR

This paper introduces neuROSym, a ROS package that deploys and evaluates neuro-symbolic human motion prediction models in real-world robot scenarios, demonstrating improved accuracy and runtime performance over neural-only models.

Contribution

The work extends a neuro-symbolic architecture into a ROS package for real-time deployment and evaluation on robots, facilitating practical use and comparison with neural models.

Findings

Neuro-symbolic models outperform neural-only baselines in accuracy.

neuROSym enables online visualization and evaluation of motion prediction.

The package is publicly available for the robotics community.

Abstract

Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatial interactions representation. This work achieved better performance than neural-only baseline architectures on offline datasets. In this paper, we extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios, which can run, visualise, and evaluate previous neural-only and neuro-symbolic models for motion prediction online. We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns. We assessed accuracy and runtime performance of the prediction models, showing a general improvement in case our neuro-symbolic architecture is used. We make the neuROSym package1 publicly available to the robotics community.