arXiv Computer Vision
Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation
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arXiv Computer Vision
Oct 12
GuidedNav: Shaping Vision-Language Navigation Representations through Pre-action Attention and Spatial Guidance
GuidedNav is a new framework for vision‑and‑language navigation that explicitly shapes the representations linking instructions to actions. It uses subtask and landmark guidance to focus pre‑action attention on the relevant instruction segment and visual regions, and spatial guidance to align internal representations with geometric features of the environment. The method achieves state‑of‑the‑art success rates on R2R‑CE and RxR‑CE val‑unseen, improving by 8.1% and 13.1% respectively, while also providing faster inference and introducing two new annotation‑enriched datasets, Guided‑R2R and Guided‑RxR.
arXiv Machine Learning
Aug 4
SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
arXiv:2608. 01899v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning.
arXiv AI
Jun 30
Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
arXiv:2606. 29908v1 Announce Type: cross Abstract: Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis.
arXiv Computer Vision
Oct 5
Imagine the Future, Internalize the Gist: Efficient VLA Reasoning via Internalized Spatiotemporal Imagination
The paper introduces IG‑VLA, a vision‑language‑action framework that learns to imagine task‑relevant future scene evolution in a latent spatiotemporal space, guiding action prediction without generating full pixel‑level videos. It further compresses this future reasoning into a compact Scene Gist Token via Scene Gist Memory, enabling efficient inference. Experiments on LIBERO, LIBERO‑Plus, and VLABench show that IG‑VLA improves success rates by nearly 6% and speeds up inference up to 6.38× compared to strong baselines.
arXiv AI
Jul 14
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
Jul 21
Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering
arXiv:2510. 01483v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memory or explicit spatial representation, all sampled frames must be re-processed for every new query.