VerNav: Verifier-First Low-Latency Vision-and-Language Navigation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 28397v1 Announce Type: cross Abstract: Vision-language navigation (VLN) has recently advanced with large language and multimodal models, enabling agents to follow natural-language instructions in unseen environments without training a task-specific navigation policy.
LookStep is a new end‑to‑end framework for Vision‑Language Navigation that integrates Language‑Centric Future State Modeling with an Event‑Driven Rolling Memory. It uses language labels to predict coarse navigation progress and future states for candidate actions, and autonomously decides which observations to store in a bounded memory with semantic roles. Empirical results show that LookStep outperforms existing methods on VLN‑CE tasks, achieving a 49.7% success rate on R2R‑CE Val‑Unseen while improving memory efficiency and reducing data requirements.
The paper introduces VLN-AIEP, a Vision‑Language Navigation framework that integrates instruction‑execution progress awareness into navigation decisions. By linking the visited trajectory to the instruction, it suppresses embeddings of already executed content and matches candidate locations with pending instruction segments. The method employs a landmark‑anchored trajectory representation to encode semantic labels and geometry, enabling progress estimation without explicit supervision and achieving strong results on R2R, RxR, CVDN, and R2R‑CE benchmarks.
arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.
arXiv:2610.35066v1 Announce Type: new Abstract: Vision-language navigation (VLN) requires an embodied agent to follow language instructions through 3D environments. Maintaining persistent episodic me...
arXiv:2607. 01754v1 Announce Type: new Abstract: On-policy exploration is a crucial component for training robust Vision-Language Navigation agents, as it exposes the policy to a broader state distribution.