arXiv Computer Vision
V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models
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
Jun 24
G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models
arXiv:2606. 24472v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their visual tokens remain grounded in 2D image coordinates rather than the calibrated geometry of the robot's cameras -- a mismatch especially pronounced in multi-camera setups, where views are coupled by known intrinsics and extrinsics yet processed as independent images.
arXiv AI
Jun 16
Action with Visual Primitives
arXiv:2605. 22183v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation.
arXiv AI
Jun 9
GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.
arXiv AI
Aug 12
Lost in Reconstruction: Aligning Action Representations with Language in Vision-Language-Action Models
arXiv:2608. 10484v1 Announce Type: cross Abstract: Action verbs describe not only the physical outcomes of actions, but also how those actions are performed.
arXiv AI
Jul 3
Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration
arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.
arXiv Machine Learning
Jul 7
XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control
arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.