Dec-MARVEL: Decentralized Multi-Agent Exploration without Communication under Budget Constraints
This paper presents Dec-MARVEL, a decentralized budget-aware exploration framework for communication-free multi-UAV teams with directional sensing. Robots coordinate by observing teammate trajectories within their field of view. Using a graph-attention actor, they select return-feasible waypoints. Experiments show superior exploration rates and minimal sensing overlap across various team sizes and budgets, with successful sim-to-real transfer.
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[Submitted on 10 Jul 2026]
Title:Dec-MARVEL: Decentralized Multi-Agent Exploration without Communication under Budget Constraints
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Abstract:Multi-UAV exploration is often constrained by unreliable communication, limited field-of-view sensing (e.g., lightweight onboard camera), and finite travel budgets that require each robot to reserve enough budget to return to its base. We present Dec-MARVEL, a decentralized budget-aware exploration framework for communication-free teams with directional sensing. Rather than exchanging maps, goals, or messages, each robot coordinates through its incidental observations: any teammate trajectory within its field of view serves as a coordination signal. A graph-attention actor fuses local frontier geometry, teammate motion, and budget features to select return-feasible waypoint-heading actions. The actor is trained with phase-conditioned critics, a training-only task-oriented privileged critic, and a mixture-based budget curriculum. Across 900 held-out trials spanning three team sizes (2, 4, 8 robots) and three travel budgets (720, 800, 1024 meters) against four baselines, Dec-MARVEL achieves the highest or tied-highest exploration rate and lowest sensing overlap across all nine team-size budget configurations. Under our tightest 720m budget, it reaches 53%, 94%, and 100% success for 2, 4, and 8 robots, versus 37%, 83%, and 99% for the strongest baseline. Physical-robot experiments demonstrate successful sim-to-real transfer and real-world deployment of Dec-MARVEL.
Comments: 8 pages, 5 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.09060 [cs.RO]
(or arXiv:2607.09060v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09060
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Janghyun Cho [view email] [v1] Fri, 10 Jul 2026 03:06:08 UTC (2,142 KB)
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