Multi-Objective Compliance-Integrated Coevolution For Simulated And Real-World Deployment Of Multi-Robot Marine Autonomy
This paper introduces MMOCIC, a multi-objective framework that blends coevolved behaviors with compliance behaviors to balance team progress and norm adherence. By decoupling learning from compliance, it achieves high performance with fewer collisions. Demonstrated on a swimmer rescue mission with up to 8 robots in hardware and 12 in simulation.
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[Submitted on 28 Jul 2026]
Title:Multi-Objective Compliance-Integrated Coevolution For Simulated And Real-World Deployment Of Multi-Robot Marine Autonomy
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Abstract:Collaborative robots are well-suited to maritime missions that benefit from coordination, such as the exploration of unknown reef structures, inspection of subsea infrastructure, or search-and-rescue operations. These missions typically provide sparse feedback signals for measuring progress and require adherence to safety and regulatory norms, turning a mission into a multi-objective optimization problem. Coevolutionary algorithms can process these sparse feedback signals to generate coordinated behaviors, and in some cases extend behaviors to multiple objectives. However, incorporating high-level team objectives with low-level compliance considerations on the fly to balance norm adherence with team performance remains elusive. This paper introduces a multi-objective framework that blends coevolved behaviors with compliance behaviors to achieve a balance between maximizing team progress and minimizing norm violations. The key insight is to decouple learning from compliance since operational norms are prescribed rather than discovered. We demonstrate that our framework achieves high team performance while avoiding collisions on a collaborative swimmer rescue mission with up to 8 vehicles in a hardware deployment, and 12 vehicles in simulation. The key contribution of this paper is Marine Multi-Objective Compliance-Integrated Coevolution (MMOCIC), a framework that blends team-wide optimization with established norms for real-world deployments of learning-based coordination.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.26279 [cs.RO]
(or arXiv:2607.26279v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.26279
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Everardo Gonzalez [view email] [v1] Tue, 28 Jul 2026 21:21:45 UTC (3,010 KB)
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