Hugging Face Trending Papers June 16, 2026

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

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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.

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arXiv Machine Learning

Jun 17

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.

By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

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 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

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

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 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

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

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

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

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

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