arXiv AI
Jul 7
Verifier-free Test-Time Sampling for Vision-Language-Action Models
arXiv:2510. 05681v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control.
SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models
arXiv:2602. 04208v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training.
Related stories
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
Aug 6
Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
arXiv Machine Learning
Jun 10
Task Robustness via Re-Labelling Vision-Action Robot Data
arXiv:2606. 10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios.
arXiv AI
Jun 29
S$^2$-VLA: State-Space Guided Vision-Language-Action Models for Long-Horizon Manipulation
arXiv:2606. 27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation.
arXiv AI
Jun 2
VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.
arXiv AI
Jun 30
Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
arXiv:2606. 29892v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become indispensable for pushing Vision-Language-Action Models (VLAs) beyond static imitation learning.
arXiv AI
Jun 9
Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis
arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.
arXiv AI
Jun 19
Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
arXiv:2512. 20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects.
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.
arXiv Machine Learning
Jun 16
AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.
arXiv AI
Jun 26
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
arXiv:2606. 27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks.