arXiv AI
Aug 5
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.
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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.
Hugging Face Trending Papers
Jun 23
NavWM: A Unified Navigation World Model for Foresight-Driven Planning
Conventional visual navigation policies often struggle with myopic decision-making and mode collapse in complex environments. While world models offer a promising alternative, existing paradigms typically isolate perception, generation, and control, failing to capture their shared spatio-temporal dynamics.
arXiv AI
Jul 17
FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models
arXiv:2607. 14739v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive results in visuomotor policy learning, yet remain fundamentally reactive, mapping current observations and language to actions without explicit forward prediction of world dynamics.
arXiv Machine Learning
Jun 17
VISTA: Scale-Aware Visual Navigation via Action History Conditioning
arXiv:2606. 17294v1 Announce Type: cross Abstract: Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments.
Hugging Face Trending Papers
Aug 10
GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction
Long-horizon future-frame prediction is important for autonomous driving, traffic surveillance, and intelligent transportation systems, yet remains challenging due to temporal ghosting, geometry drift, and inconsistent object motion. Recent latent video diffusion models have achieved impressive visual quality, but directly applying them to structured traffic scenes often leads to unstable geometry and degraded temporal coherence over extended horizons.
arXiv AI
Jun 8
Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation
arXiv:2606. 07244v1 Announce Type: cross Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments.
arXiv AI
Jun 6
WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation
arXiv:2606. 06147v1 Announce Type: new Abstract: End-to-end Vision-Language-Action (VLA) models have shown promise in UAV navigation.
arXiv Machine Learning
Jun 17
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
arXiv AI
Jun 30
LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.
arXiv AI
Aug 10
WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN
arXiv:2608. 07267v1 Announce Type: new Abstract: Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions.
arXiv AI
1d ago
VTInstructor: Visual Trajectory Prompting for Navigation Instruction Generation in Continuous Environments
arXiv:2608. 15284v1 Announce Type: cross Abstract: Navigation instruction generation from ego-centric RGB video in continuous environments is an important yet challenging task for human-robot interaction and scalable dataset construction.