GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2509.10454v1
• Date: Fri, 12 Sep 2025 17:59:58 GMT
• Title: GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
• Authors: Hang Yin, Haoyu Wei, Xiuwei Xu, Wenxuan Guo, Jie Zhou, Jiwen Lu,
• Abstract summary: We propose a training-free framework for vision-and-language navigation (VLN)<n>Our framework formulates navigation guidance as graph constraint optimization by decomposing instructions into explicit spatial constraints.<n>Our framework can effectively generalize to new environments and instruction sets, paving the way for a more robust and autonomous navigation framework.
• Score: 61.34589819350429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a training-free framework for vision-and-language navigation (VLN). Existing zero-shot VLN methods are mainly designed for discrete environments or involve unsupervised training in continuous simulator environments, which makes it challenging to generalize and deploy them in real-world scenarios. To achieve a training-free framework in continuous environments, our framework formulates navigation guidance as graph constraint optimization by decomposing instructions into explicit spatial constraints. The constraint-driven paradigm decodes spatial semantics through constraint solving, enabling zero-shot adaptation to unseen environments. Specifically, we construct a spatial constraint library covering all types of spatial relationship mentioned in VLN instructions. The human instruction is decomposed into a directed acyclic graph, with waypoint nodes, object nodes and edges, which are used as queries to retrieve the library to build the graph constraints. The graph constraint optimization is solved by the constraint solver to determine the positions of waypoints, obtaining the robot's navigation path and final goal. To handle cases of no solution or multiple solutions, we construct a navigation tree and the backtracking mechanism. Extensive experiments on standard benchmarks demonstrate significant improvements in success rate and navigation efficiency compared to state-of-the-art zero-shot VLN methods. We further conduct real-world experiments to show that our framework can effectively generalize to new environments and instruction sets, paving the way for a more robust and autonomous navigation framework.

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