arXiv AI
LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation
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SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning
arXiv:2606. 08992v1 Announce Type: cross Abstract: Vision-and-Language Navigation in continuous environments requires agents to understand the spatial structure of previously unseen environments in order to follow language instructions.
arXiv AI
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ABot-N1: Toward a General Visual Language Navigation Foundation Model
arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.
arXiv AI
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Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
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arXiv AI
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Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training
arXiv:2512. 24125v3 Announce Type: replace-cross Abstract: General-purpose robotic systems operating in open-world environments must achieve both broad generalization and high-precision action execution, a combination that remains challenging for existing Vision-Language-Action (VLA) models.
arXiv Computer Vision
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Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
The paper introduces GTA‑VLA, an interactive Vision‑Language‑Action framework that lets users guide robot policies with explicit visual cues such as affordance points, boxes, and traces. Unlike traditional direct sense‑to‑act models, GTA‑VLA incorporates a spatial‑visual Chain‑of‑Thought that blends human guidance with internal task planning, and couples this reasoning module with a lightweight reactive action head for efficient execution. Experiments on the SimplerEnv WidowX benchmark show a state‑of‑the‑art 81.2 % success rate, and the framework significantly improves task success under out‑of‑domain visual shifts and spatial ambiguities, demonstrating the benefit of interactive reasoning for failure recovery in embodied control.
arXiv AI
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Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.