Title: SCENE: Multi-Robot Active Cognition via Unified Free-Energy Minimization
Primary area: applications to robotics, autonomy, planning
Keywords: Multi-Robot Systems, Active Exploration, Active Cognition, Free Energy Principle, Neural Implicit Representation, Semantic Exploration, Age of Information (AoI)

Abstract: Existing multi-robot exploration methods are fragmented, leading to suboptimal decisions in complex environments. We introduce SCENE, a framework that unifies decision-making by reframing exploration as cognitive free-energy minimization. Our core contribution is a free-energy formulation that integrates geometric uncertainty, semantic novelty, and the Age of Information (AoI). The system leverages a neural implicit field for unified representation, a prototypical network for open-world semantics, and a Graph Neural Network (GNN) for emergent collaboration. Trained end-to-end via self-supervision, SCENE surpasses state-of-the-art methods in challenging scenarios, demonstrating superior efficiency and decision-making intelligence.

TL;DR: SCENE unifies multi-robot exploration by minimizing a single free-energy objective that balances geometric, semantic, and temporal goals.