We are pleased to announce that the article “Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation” has been accepted at the 2025 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN). This work was carried out under euROBIN’s WP7, led by PhD student Kevin Alcedo and co-supervised by Pedro U. Lima and Rachid Alami.
Robots navigating alongside humans must not only reason under uncertainty but also account for the beliefs, intentions, and perspectives of those around them. Traditional egocentric navigation can be formulated as a Markov Decision Process (MDP), yet social navigation requires extending this to a Partially Observable MDP (POMDP), where agents lack direct access to others' mental states. To address this challenge, the paper proposes a neuro-symbolic, model-based reinforcement learning architecture that enables effective belief tracking in partially observable environments. It further introduces a perspective-shift operator for belief estimation, while also leveraging recent advances in Influence-based Abstractions (IBA) to improve reasoning in structured multi-agent settings.
This contribution opens new directions for socially-aware navigation, bridging symbolic reasoning and neural learning methods in robotics.
More information about the article: https://arxiv.org/abs/2503.20425
Congratulations to the authors Kevin Alcedo, Pedro U. Lima, and Rachid Alami for their innovative work!