arXiv Machine Learning
Can Knowledge Transfer Parameters Be Learned? LePoKet for Efficient Robotic Vision
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arXiv Machine Learning
Sep 23
HABILIS Brain 0: Geometry-Change Supervision for Vision-Language-Action and Residual Flow Recovery
arXiv:2609.25558v1 Announce Type: cross Abstract: Vision-language-action policies benefit from geometric supervision, but current-frame geometry alone does not explicitly describe the changes associa...
arXiv AI
Aug 13
G0.5: One Autoregressive Stream for Robot Reasoning and Action
arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.
arXiv Machine Learning
Sep 1
AdaVLA: Adaptive Step Flow Matching for Training-free Acceleration of Vision-Language-Action Models
arXiv:2608.29208v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models, built upon Vision-Language Models (VLMs), have significantly enhanced robotic capabilities by leveraging interne...
arXiv Computer Vision
Sep 24
Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation
Task-Prototype Guided Flow Matching (TP-Flow) is a few‑shot manipulation framework that transforms support demonstrations into structured task‑prototype tokens to guide both the initial flow prior and the velocity field. It uses symmetric cross‑attention with learnable queries to extract phase‑level prototypes, parameterizes a task‑adaptive initial distribution, and injects prototype information through gated adaptive normalization. TP‑Flow is trained with an episodic support‑query objective and prototype contrastive regularization, achieving high success rates on the LEROBOT‑ARM‑SO101 platform while maintaining real‑time execution and low latency.
arXiv Machine Learning
Jun 4
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
arXiv AI
4d ago
V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...