A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

• URL: http://arxiv.org/abs/2304.11740v1
• Date: Sun, 23 Apr 2023 20:11:40 GMT
• Title: A Neuro-Symbolic Approach for Enhanced Human Motion Prediction
• Authors: Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto
• Abstract summary: We propose a neuro-symbolic approach for human motion prediction (NeuroSyM) NeuroSyM weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called qualitative Trajectory Calculus (QTC) Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.
• Score: 5.742409080817885
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.

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