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ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
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Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. It becomes increasingly crucial in the field of embodied AI, with potential applications in autonomous navigation, search and rescue, and human-robot interaction. In this paper, we propose to address a more practical yet challenging counterpart setting - vision-language navigation in continuous environments (VLN-CE). To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav performs online topological mapping of environments by self-organizing predicted waypoints along a traversed path, without prior environmental experience. It privileges the agent to break down the navigation procedure into high-level planning and low-level control. Concurrently, ETPNav utilizes a transformer-based cross-modal planner to generate navigation plans based on topological maps and instructions. The plan is then performed through an obstacle-avoiding controller that leverages a trial-and-error heuristic to prevent navigation from getting stuck in obstacles. Experimental results demonstrate the effectiveness of the proposed method. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets, respectively. Our code is available at https://github.com/MarSaKi/ETPNav.
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Cited by 6 Pith papers
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Joint On-and-Off Policy Learning for Vision-and-Language Navigation
JOP-VLN combines DAgger imitation learning with GRPO reinforcement learning, using high-entropy trajectory filtering and error-correction prioritization, achieving 69.9% SR on R2R Val-Unseen.
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StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling
A streaming navigation framework combining a sliding-window KV cache with depth-based token pruning achieves state-of-the-art results on VLN-CE benchmarks with bounded context and low latency.
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Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
Iterative navigator-generator collaboration, where the navigator filters generated instructions and the rebuilt generator rewrites low-quality ones, raises R2R navigation SPL to 78% and instruction SPICE to 26.2.
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SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts
One navigation model with state-adaptive mixture-of-experts routing matches or exceeds task-specific agents on several of seven navigation benchmarks.
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Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation
MFRA combines a hierarchical DIRformer-style fusion backbone with instruction-guided attention and a GRU history encoder, reporting improved VLN benchmark scores.
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NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation
NavAgent fuses fine-grained landmark detection, a growing scene topology map, and an LLM to improve outdoor vision-and-language navigation, outperforming VELMA on Touchdown and Map2seq.
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