DM$^3$-Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation

arXiv cs.RO / 4/27/2026

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Key Points

  • The paper introduces DM$^3$-Nav, a fully decentralized multi-robot semantic navigation system that handles multimodal, open-vocabulary goal definitions and multi-object tasks.
  • Robots coordinate without any central coordinator, relying on unsynchronized ad-hoc pairwise communication to exchange local maps, goal status, and navigation intent.
  • An implicit task-allocation method—combining intent broadcasting with distance-weighted frontier selection—aims to reduce duplicate exploration while keeping decentralization.
  • Experiments on HM3DSem (using HM3Dv0.2 and GOAT-Bench) show DM$^3$-Nav performs on par with or better than centralized/shared-map baselines while removing single points of failure.
  • The authors also report real-world deployment with two mobile robots in an office setting, successfully running using only onboard sensing and computation.

Abstract

We present DM^3-Nav, a fully decentralized multi-agent semantic navigation system supporting multimodal open-vocabulary goal specification and multi-object missions. In our setting, decentralization implies operation without a central coordinator, global map aggregation, or shared global state at runtime. Robots operate autonomously and coordinate through ad-hoc pairwise communication, exchanging local maps, goal status, and navigation intent without synchronization. An implicit task allocation mechanism combining intent broadcasting and distance-weighted frontier selection reduces redundant exploration while preserving decentralized operation. Evaluations on HM3DSem scenes using the HM3Dv0.2 and GOAT-Bench datasets demonstrate that DM^3-Nav matches or exceeds centralized and shared-map baselines while eliminating single points of failure inherent in centralized architectures. Finally, we validate our approach in a real-world office environment using two mobile robots, demonstrating successful deployment relying entirely on onboard sensing and computation. A video of our real-world experiments is available online: https://drive.google.com/file/d/1QiUSCn5rIvtuTUqtuXLPgmt6S8x9-MCZ/view?usp=drive_link