arXiv AI

FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding

arXiv Machine Learning

Aug 21

HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models

arXiv:2602. 13710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots and edge platforms.

By Xin Yan, Zhenglin Wan, Feiyang Ye, Xingrui Yu, Hangyu Du, Yang You, Ivor Tsang

robotics efficiency multimodal benchmarks

arXiv AI

Jun 8

ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models

arXiv:2605. 24011v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical.

By Arash Akbari, Arman Akbari, Masih Eskandar, Qitao Tan, Yixiao Chen, Jingwu Luo, Bertha Pangaribuan, Liyun Zhang, Jennifer Dy, Geng Yuan, Xue Lin, Gaowen Liu, Stratis Ioannidis, Yanzhi Wang

agents robotics efficiency multimodal benchmarks

arXiv Machine Learning

Aug 10

{\Omega}-QVLA: Robust Quantization for Vision-Language-Action Models via Composite Rotation and Per-step Scaling

arXiv:2605. 28803v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive.

By Xinyu Wang, Mingze Li, Sicheng Lyu, Dongxiu Liu, Kaicheng Yang, Ziyu Zhao, Yufei Cui, Xiao-Wen Chang, Peng Lu

llms diffusion robotics efficiency multimodal

arXiv Machine Learning

Aug 12

HoloQ-VLA: Uniform W4A4 Quantization of Vision-Language-Action Models

arXiv:2605. 28803v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models unify perception, reasoning, and control in a single policy, but their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive.

By Xinyu Wang, Mingze Li, Sicheng Lyu, Dongxiu Liu, Kaicheng Yang, Ziyu Zhao, Yufei Cui, Xiao-Wen Chang, Peng Lu

diffusion robotics efficiency multimodal

arXiv AI

Aug 18

Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification

arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.

By Chunyu Qi, Zhuoran Song, Jian Weng, Haozhe Jiang, Xueyuan Liu, Naifeng Jing, Guanghui He, Xiaoyao Liang, Haibing Guan

robotics efficiency multimodal benchmarks

arXiv Machine Learning

Jul 31

QuantWAMs: Calibrating at the Right Granularity for World Action Models

arXiv:2607. 28405v1 Announce Type: cross Abstract: World Action Models (WAMs) jointly predict future observations and actions, but their iterative denoising and closed-loop execution make efficient deployment costly.

By Jiacheng Zhou, Jinfan Lv, Ruixuan Li, Longtai Zhang, Yan Wang, Wenqiang Zhang, Lizhe Qi

robotics efficiency

arXiv AI

Sep 23

vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

arXiv:2609.24274v1 Announce Type: cross Abstract: Deploying language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between polic...

By Khanh D. Nguyen, Hoang M. Truong, An T. Le

robotics

Hugging Face Trending Papers

Jul 29

TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

Vision-language-action (VLA) models commonly adopt an LLM-centric $V \to L \to A$ pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.

llms robotics efficiency multimodal

arXiv AI

Jun 16

X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining

arXiv:2606. 14752v1 Announce Type: cross Abstract: Modern Vision-Language-Action (VLA) models must bridge pretrained vision-language reasoning and precise continuous robot control.

By Xirui Kang, Yanpei Shi, Lucy Liang, Roy Gan, Dongxiu Liu, Pushi Zhang, Danpeng Chen, Xiaoyi Qin, Yinan Zheng, Jinliang Zheng, Hao Wang, Xianyuan Zhan, Hang Su

nlp robotics efficiency multimodal safety

arXiv AI

Jul 28

A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference

arXiv:2607. 24148v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment.

By Zhuoran Song, Haozhe Jiang, Chunyu Qi, Minnan Pei, Gang Li, Xiaoyao Liang, Haibing Guan

robotics efficiency multimodal

arXiv AI

Jul 15

Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference

arXiv:2607. 12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks.

By Zebin Yang, Qi Wang, Yunhe Wang, Xiurui Guo, Bo Yu, Shaoshan Liu, Jiafeng Xu, Hao Dong, Meng Li

llms robotics efficiency multimodal benchmarks

Hugging Face Trending Papers

Jul 27

A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference

Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment. Existing accelerators, such as Dadu-Corki, improve efficiency but treat VLA models as full-precision workloads, leaving substantial redundancy in both memory and computation underexploited.

robotics efficiency multimodal

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