neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction
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.
Evidence notes
- Experiments use TIAGo as the physical evaluation platform.
- The reported results provide an independent evaluation on TIAGo hardware.
Company context
Develops service and humanoid robots for research, industrial, and public-facing environments, with a focus on modular platforms and long-term deployment in real-world settings.