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.
Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs
arXiv:2606. 10180v1 Announce Type: cross Abstract: We introduce flow control of vision-language-action (VLA) models, a simple and effective way to steer VLA actions in real-time through generic inputs, such as a keyboard.
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arXiv Machine Learning
Jul 17
DiMaS: Distribution Matching for Steering Vision-Language-Action Models
arXiv:2607. 14280v1 Announce Type: cross Abstract: Flow-matching-based vision-language-action (VLA) models have emerged as powerful policies for robotic manipulation, yet a critical capability remains underexplored: fine-grained behavioral control, the ability to govern how a robot performs a task by intervening on its internal representations.
arXiv Machine Learning
Jun 11
Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.
arXiv AI
Jul 9
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
arXiv:2507. 05116v5 Announce Type: replace-cross Abstract: Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language.
arXiv AI
2d ago
NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation
arXiv:2608. 16503v1 Announce Type: cross Abstract: Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness.
arXiv AI
Jun 2
Closed-Loop Neural Activation Control in Vision-Language-Action Models
arXiv:2606. 00269v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use a fixed steering coefficient, effectively operating in open loop.
arXiv AI
Jul 1
Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
arXiv:2606. 31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control.
Hugging Face Trending Papers
Jul 7
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based VLA models have shown remarkable success due to their capability to generate precise and smooth action sequences and capture multimodal distributions.
Hugging Face Trending Papers
Jul 27
DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.
arXiv AI
Jun 9
CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
arXiv:2606. 09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution.
arXiv Machine Learning
Jul 8
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
arXiv:2607. 06370v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations.
arXiv AI
Jun 19
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.