V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2608.25308v1
• Date: Wed, 26 Aug 2026 02:32:18 GMT
• Title: V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models
• Abstract summary: We propose V-Link, which explicitly recovers visual representations during the vision-language (VL) to action (A) feature transfer.<n>We show that V-Link improves the average success rate over base model GR00T N1.6 by +1.9%, +31.2%, and +18.8%, respectively.<n>On the AGIBOT A3 Ultra, V-Link further achieves gains of +20% and +24% on two real-world humanoid tasks.
• Score: 19.466602886959866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language-action (VLA) models provide a scalable path toward generalist robotic manipulation by integrating visual perception, language understanding, and continuous action control. However, we reveal a critical limitation of VLA architectures: the action expert has limited access to the 3D geometric and 2D semantic information available in VLM features. This accessibility gap weakens perceptual grounding and limits performance on fine-grained robotic manipulation. To address this issue, we propose V-Link, which explicitly recovers visual representations during the vision-language (VL) to action (A) feature transfer. Specifically, V-Link learns complementary Spatial and Semantic Query representations within the VLM and injects them into Action DiT through asymmetric pathways. Semantic Queries complement the original VLM image tokens, whereas Spatial Queries provide dedicated geometric conditioning for spatially grounded action generation. Across LIBERO, LIBERO-Plus, and RoboTwin 2.0, our V-Link improves the average success rate over base model GR00T N1.6 by +1.9%, +31.2%, and +18.8%, respectively. On the AGIBOT A3 Ultra, V-Link further achieves gains of +20% and +24% on two real-world humanoid tasks.

Related papers

• GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation [49.16739604572808]
  Vision-Language-Action (VLA) models achieve strong benchmark performance but struggle in real-world deployment with unseen objects.<n>We argue that this stems from the lack of a unified geometry-aware manipulation representation.<n>We propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation.
  arXiv  Detail & Related papers  (2026-06-07T09:23:16Z)
• Two Bridges, One Pathway: From VLMs to Generalizable VLAs with Embodied Trajectory-Coupled Data [93.7685703383343]
  Vision-language models (VLMs) are powerful general-purpose reasoners, yet converting them into robot control policies is surprisingly difficult.<n>We argue that this gap can be bridged gradually with the right intermediate data.<n>We show that mixing task-relevant out-of-distribution ETC data with a small amount of action data enables the model to generalize to novel visual-language conditions.
  arXiv  Detail & Related papers  (2026-06-07T08:57:51Z)
• PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction [57.63073414949329]
  We propose PointACT, a dual-system 3D-aware VLA policy that integrates hierarchical 3D point cloud representations directly into the action decoding process.<n>PointACT employs a multi-scale point-action interaction mechanism with efficient bottleneck window self-attention, enabling evolving action tokens to densely attend to both local geometric detail and global scene structure.
  arXiv  Detail & Related papers  (2026-05-20T17:10:31Z)
• Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation [67.16145181790522]
  This paper tackles spatial perception and manipulation challenges in Vision-Language-Action (VLA) models.<n>We propose a Geometry-Guided Gated Transformer (G3T) that aligns multi-view features under 3D geometric guidance.<n>To improve action learning efficiency, we introduce Action Manifold Learning (AML), which directly predicts actions on the valid action manifold.
  arXiv  Detail & Related papers  (2026-05-12T09:21:29Z)
• IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance [35.90972175891154]
  Many Vision-Language-Action (VLA) models flatten image patches into a 1D token sequence, weakening the 2D spatial cues needed for precise manipulation.<n>We introduce a lightweight, training-free method that improves spatial understanding by exploiting affinity hints already available in the model's built-in vision.
  arXiv  Detail & Related papers  (2026-01-22T18:57:13Z)
• Robotic VLA Benefits from Joint Learning with Motion Image Diffusion [114.60268819583017]
  Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions.<n>We propose joint learning with motion image diffusion, a novel strategy that enhances VLA models with motion reasoning capabilities.<n> Experiments in both simulation and real-world environments demonstrate that joint learning with motion image diffusion improves the success rate of pi-series VLAs to 97.5%.
  arXiv  Detail & Related papers  (2025-12-19T19:07:53Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)
• PointVLA: Injecting the 3D World into Vision-Language-Action Models [10.758939578236582]
  We propose PointVLA, a framework that enhances pre-trained vision-language-action models with point cloud inputs without requiring retraining.<n>Our method freezes the vanilla action expert and injects 3D features via a lightweight modular block.<n>PointVLA outperforms state-of-the-art 2D imitation learning methods across both simulated and real-world robotic tasks.
  arXiv  Detail & Related papers  (2025-03-10T16:32:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.