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Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments
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In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent's location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
Forward citations
Cited by 4 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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Efficient-VLN: A Simple yet Strong Baseline for Efficient Vision-Language Navigation
The submitted abstract, the paper body, and the conclusion report incompatible headline results (73.2/75.6, 64.2/67.0, and 62.3/64.5 SR on R2R-CE/RxR-CE), so the claimed state of the art is not internally consistent.
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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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VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning
VLN-R1 shows that a Qwen2-VL model trained with supervised fine-tuning plus GRPO-based reinforcement fine-tuning can navigate continuous indoor environments from egocentric video, improving success rate on VLN-CE R2R ...
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