arXiv Machine Learning

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.

Hugging Face Trending Papers

Jun 16

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable.

diffusion robotics fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 25

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

arXiv:2512.01946v4 Announce Type: replace-cross Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in r...

By Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid

llms robotics multimodal benchmarks

arXiv Machine Learning

Sep 1

AdaVLA: Adaptive Step Flow Matching for Training-free Acceleration of Vision-Language-Action Models

arXiv:2608.29208v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models, built upon Vision-Language Models (VLMs), have significantly enhanced robotic capabilities by leveraging interne...

By Sunghwan Han, Youngtae Han, Youngmin Yi

llms diffusion robotics fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 3

Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies

arXiv:2607. 02092v1 Announce Type: cross Abstract: Flow-matching vision-language-action policies generate robot action chunks through an iterative transport process, creating an opportunity for test-time guidance without retraining the base policy.

By Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng, Zezhi Tang

robotics multimodal

arXiv Machine Learning

Jul 13

FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

arXiv:2607. 08877v1 Announce Type: cross Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks.

By Michael Murray, Daphne Chen, Simran Bagaria, Dean Fortier, Tess Hellebrekers, Galen Mullins, Harshavardhan Gajarla, Oier Mees, Maya Cakmak, Andrey Kolobov

diffusion reinforcement-learning robotics fine-tuning

arXiv Computer Vision

2d ago

NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields

arXiv:2610.00981v1 Announce Type: cross Abstract: We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric repr...

By Shota Kobayashi, Koki Seno, Daichi Yashima, Komei Sugiura

robotics multimodal

arXiv Machine Learning

Jun 5

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

arXiv:2606. 05758v1 Announce Type: cross Abstract: Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens.

By Zhuoming Liu, Jinhong Lin, Kwan Man Cheng, Lin Zhang, Shayok Bagchi, Yin Li

llms diffusion robotics multimodal

arXiv AI

Aug 7

The Geometry of Flow-Matching Uncertainty: A Cost-free Uncertainty Proxy and Its Application in Flow-based VLA Failure Detection

arXiv:2607. 27933v3 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.

By Ziyang Rao, Yiren Zhao, Weiyu Guo, Ben Fei, Yandong Guo, Hui Xiong

diffusion robotics

arXiv Machine Learning

Sep 10

LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

arXiv:2608.25757v4 Announce Type: replace-cross Abstract: Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: a...

By Jin Lou, Zhiyuan Jing, Xupeng Wang, Andong Chen, Xingdong Zhu, Yuexuan Li, Yuan Xu, Zhijie Zhu, Yingwei Ji, Wenpeng Nie, Renxing Feng, Liangliang Chen, Ying Chu, Jingyi Li, Jinyan Liu, Zhiqi Song, Jingxuan Zhu, Jidong Zhang, Yufei Liu, Boyang Xing, Lei Jiang, Yan Cui, Hongming Li, Yuchen Zhu

robotics

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.

By Yi Yu, Xinchuan Qiu

robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 21

RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

arXiv:2510. 00037v5 Announce Type: replace-cross Abstract: In Vision-Language-Actionf(VLA) models, robustness to real-world perturbations is critical for deployment.

By Jianing Guo, Zhenhong Wu, Chang Tu, Yiyao Ma, Xiangqi Kong, Zhiqian Liu, Jiaming Ji, Shuning Zhang, Yuanpei Chen, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Huijie Zhao, Weifeng Lv, Simin Li

llms diffusion reinforcement-learning robotics multimodal safety

arXiv Machine Learning

Jun 5

Is Diversity All You Need for Scalable Robotic Manipulation?

arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood.

By Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li

computer-vision nlp robotics fine-tuning

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