arXiv Computer Vision
NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Sep 16
Seeing What Matters: Visual Cue Guided Video Planning for Generalizable Robot Navigation
arXiv:2609.16737v1 Announce Type: cross Abstract: Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches of...
arXiv AI
Jun 30
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
arXiv AI
Sep 17
Visual Cue Guided Video Planning for Generalizable Robot Navigation
CueNav is a video model-based navigation framework that uses visual cues—a Bird's-Eye View map for global task context and a body-aware egocentric view for embodiment context—to guide a video planner. The framework couples this planner with an embodiment-specific Inverse-Dynamics Model that translates dense flow fields from the video plan into robot actions. Experiments show that CueNav nearly doubles maze navigation success compared to cue-less planning and achieves 70% success in narrow passages, while also supporting zero-shot semantic-conditioned navigation across different robot platforms.
Hugging Face Trending Papers
Sep 15
Seeing What Matters: Visual Cue Guided Video Planning for Generalizable Robot Navigation
Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches often condition video planning on short-horizon guida...
arXiv Computer Vision
3d ago
CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization
The paper introduces CASTER, a framework that formalizes the conversion of synthetic videos into real robot trajectories as a constrained optimization problem. It shows that synthetic videos must probabilistically seed the search within a semantic basin of attraction, allowing local trajectory refinement while maintaining task alignment. By removing simplifying assumptions from existing methods, the authors achieve more robust zero‑shot manipulation and improve task success by 34.1% over the best baseline.
arXiv Machine Learning
Jun 19
Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation
arXiv:2606. 20118v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution.