arXiv AI

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.

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.

By Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha, Khoa Vo, Philip Lund M{\o}ller, Quang T. Nguyen, Long Dinh, Tuan Dam, Vu Duong, Tung M. Luu, Trung Le, Tran Nguyen Le, Minh Vu, An Thai Le, Ngan Le, Daniel Sonntag, James Zou, Jan Peters, Duy M. H. Nguyen, Ngo Anh Vien

robotics fine-tuning multimodal benchmarks safety

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.

diffusion robotics efficiency multimodal

arXiv AI

2d ago

Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation

arXiv:2608. 14379v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation.

By Yuxuan Chen, Wanruo Zhang, Xiao Li

robotics multimodal benchmarks

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

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.

By Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang

robotics efficiency multimodal benchmarks

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.

By Lang Cao, Renhong Chen, Luyi Li, Peng Wang, Mofan Peng, Yitong Li

reinforcement-learning robotics fine-tuning multimodal benchmarks

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 Machine Learning

Aug 4

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

arXiv:2607. 06370v2 Announce Type: replace-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

robotics efficiency multimodal

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

arXiv AI

Jun 16

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.

By Hyeonjun Kim, Jegwang Ryu, Sangbeom Ha, Junhyeok Lee, Jun-Hyuk Kim, Hyemin Ahn, Jaeho Lee

robotics multimodal benchmarks

arXiv Machine Learning

Jun 2

Contrastive Representation Regularization for Vision-Language-Action Models

arXiv:2510. 01711v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs).

By Taeyoung Kim, Jimin Lee, Myungkyu Koo, Dongyoung Kim, Kyungmin Lee, Changyeon Kim, Younggyo Seo, Jinwoo Shin

llms robotics multimodal benchmarks

arXiv Machine Learning

Jun 9

C$^3$ache: Accelerating World Action Models with Cross Inference Chunk Cache

arXiv:2606. 08962v1 Announce Type: new Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations.

By Weisen Zhao, Lam Nguyen, Zhicong Lu, Yuzhang Shang

robotics multimodal benchmarks

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