# A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

arXiv: https://arxiv.org/abs/2304.11740 · HTML: https://mlsanity.com/paper/2304.11740/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [Causal Influence Detection for Improving Efficiency in Reinforcement Learning](https://mlsanity.com/paper/2106.03443/) (2021)
- [Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction](https://mlsanity.com/paper/2008.00777/) (2020)
- [Social and Scene-Aware Trajectory Prediction in Crowded Spaces](https://mlsanity.com/paper/1909.08840/) (2019)
- [Deep Local Trajectory Replanning and Control for Robot Navigation](https://mlsanity.com/paper/1905.05279/) (2019)
- [Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions](https://mlsanity.com/paper/2210.16535/) (2022)
- [JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments](https://mlsanity.com/paper/1910.11792/) (2019)
- [REFORM: Recognizing F-formations for Social Robots](https://mlsanity.com/paper/2008.07668/) (2020)

## Built on by (most cited first, 4 of 4)

- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios](https://mlsanity.com/paper/2406.04955/) (2024)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)
- [neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction](https://mlsanity.com/paper/2407.01593/) (2024)

## Abstract (arXiv)

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.