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CoRelNav addresses a critical gap in multi-robot systems by coupling distributed exploration with collaborative verification for semantic navigation tasks involving relational constraints. The approach demonstrates how aggregating partial observations across a team can resolve ambiguities that single agents cannot, validated through both simulation and deployment on physical robots. This work advances the field of robotics by integrating complex spatial reasoning directly into collaborative navigation strategies.
CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation
arXiv:2609.27720v1 Announce Type: new Abstract: Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.
Abstract:Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.
Robotics (cs.RO)
arXiv:2609.27720 [cs.RO]
(or arXiv:2609.27720v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.27720 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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