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

arXiv: https://arxiv.org/abs/2407.01593 · HTML: https://mlsanity.com/paper/2407.01593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [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)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [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)
- [TH"OR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset](https://mlsanity.com/paper/1909.04403/) (2019)
- [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)
- [REFORM: Recognizing F-formations for Social Robots](https://mlsanity.com/paper/2008.07668/) (2020)
- [Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration](https://mlsanity.com/paper/2001.09550/) (2020)
- [Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks](https://mlsanity.com/paper/2006.08429/) (2020)
- [A Neuro-Symbolic Approach for Enhanced Human Motion Prediction](https://mlsanity.com/paper/2304.11740/) (2023)

## Abstract (arXiv)

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.