arXiv AI June 16, 2026 By Hyeonjun Kim, Jegwang Ryu, Sangbeom Ha, Junhyeok Lee, Jun-Hyuk Kim, Hyemin Ahn, Jaeho Lee

Learned Image Compression for Vision-Language-Action Models

arXiv:2606. 16253v1 Announce Type: cross Abstract: Vision-language-action (VLA) models increasingly rely on high-frequency multi-camera observations, making visual communication a major bottleneck for real-time robotic control in bandwidth-constrained or distributed deployment settings.

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arXiv Machine Learning

Jul 21

Patch Policy: Efficient Embodied Control via Dense Visual Representations

arXiv:2607. 18236v1 Announce Type: cross Abstract: Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning.

By Gaoyue Zhou, Zichen Jeff Cui, Ada Langford, Bowen Tan, Yann LeCun, Lerrel Pinto

llms robotics fine-tuning efficiency multimodal benchmarks

Hugging Face Trending Papers

Jul 20

Patch Policy: Efficient Embodied Control via Dense Visual Representations

Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning. Modern robot policies either compress each observation into a single global token, or rely on visual backbones trained from scratch, sacrificing both fine-grained spatial detail and the benefits of large-scale visual pre-training.

llms robotics fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 17

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

arXiv:2506. 17639v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation.

By Yuxuan Chen, Yixin Han, Yize Huang, Xiao Li

reinforcement-learning robotics fine-tuning efficiency multimodal

arXiv AI

Jun 30

SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance

arXiv:2606. 30113v1 Announce Type: cross Abstract: Discrete action tokenization provides a compact interface for autoregressive VLA policies, but accurately recovering continuous robot actions from discrete codes remains challenging.

By Tengyue Jiang, Chunpu Xu, Jiayue Kang, Yao Mu

llms nlp robotics multimodal

arXiv Computer Vision

Aug 25

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

arXiv:2608.22419v1 Announce Type: cross Abstract: Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that...

By Dongzhou Cheng, Ziang Li, Yixiao Zhou, Haojuan Li, Jinghao Zhang, Lei Lei, Minjing Dong, Jie Gui, Jiaqi Wang

robotics multimodal

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.

By Ryuji Oi, Hikari Otsuka, Kosuke Matsushima, Yuki Ichikawa, Masato Motomura, Tatsuya Kaneko, Daichi Fujiki

diffusion robotics efficiency multimodal

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Patch Policy: Efficient Embodied Control via Dense Visual Representations

Patch Policy: Efficient Embodied Control via Dense Visual Representations

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement