SEEK-Multi: Collaborative Multi-Agent Semantic Reasoning for Object Goal Navigation in Inspection Tasks

We address the fundamental challenge of collaborative multi-agent object-goal navigation for autonomous inspections in complex, real-world environments. While single- agent approaches to object-goal navigation have demonstrated considerable promise in recent years, scaling these methods to larger environments necessitates the coordination of multiple robots to achieve efficient coverage, faster task completion, and robust operation under uncertainty. We introduce SEEK-Multi, a comprehensive framework that extends semantic-guided object inspection to multi-robot systems through distributed belief sharing, collaborative planning, coordinated task allocation, and adaptive communication protocols. SEEK-Multi enables multiple agents to share semantic understanding and inspection findings through a distributed Relational Semantic Network (RSN) and a shared Dynamic Scene Graph (DSG), maintaining consistency across the team while accommodating communication constraints. We propose novel algorithms for collaborative exploration that leverage semantic priors, belief fusion using consensus protocols with provable convergence guarantees, and conflict-free task allocation based on auction mechanisms. Our extensive simulation analyses across diverse environment configurations demonstrate that SEEK-Multi achieves significant speedup over single-agent approaches while maintaining high success rates, with near-linear scaling efficiency for up to four agents and graceful degradation under communication failures. We validate our approach through comprehensive simulations including ablation studies, sensitivity analyses, and comparisons with state-of-the-art multi-agent coordination methods, demonstrating its practicality for real-world multi-robot inspection scenarios in industrial, search-and-rescue, and domestic environments. Code is available at: https://arrdel.github.io/seek-multi/