arXiv Machine Learning

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.

arXiv Machine Learning

Jun 10

Task Robustness via Re-Labelling Vision-Action Robot Data

arXiv:2606. 10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios.

By Artur Kuramshin, \"Ozg\"ur Aslan, Cyrus Neary, Glen Berseth

llms robotics multimodal

arXiv AI

Jun 8

ViVa: A Video-Generative Value Model for Robot Reinforcement Learning

arXiv:2604. 08168v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback.

By Jindi Lv, Hao Li, Jie Li, Fankun Kong, Yang Wang, Pengfei Yi, Yifei Nie, Xiaofeng Wang, Zheng Zhu, Chaojun Ni, Qiuping Deng, Hengtao Li, Jiancheng Lv, Guan Huang

llms reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jul 31

DLAM: Distributional Latent Actions with Temporal Constraints

arXiv:2607. 27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change.

By Zuojin Tang, Feifan Luo, Haoyun Liu, Botai Yuan, Dekang Qi, Ronghan Chen, Yandan Yang, Tong Lin, Xinyuan Chang, Mu Xu, Bin Liu, De Ma, Zhiheng Ma

robotics multimodal

arXiv AI

Jul 10

LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action

arXiv:2607. 08182v1 Announce Type: cross Abstract: Vision-language-action (VLA) models aim to map multimodal inputs to robot actions.

By Qi Lyu, Baicheng Liu, Xudong Wang, Jiahua Dong, Lianqing Liu, Zhi Han

agents diffusion robotics multimodal benchmarks

arXiv AI

Jul 3

CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation

arXiv:2607. 02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored.

By Haokun Liu, Zhaoqi Ma, Yicheng Chen, Wentao Zhang, Masaki Kitagawa, Zicen Xiong, Jinjie Li, Moju Zhao

robotics multimodal benchmarks

arXiv AI

Jun 30

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.

By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem

llms agents diffusion robotics multimodal benchmarks safety

arXiv AI

Aug 6

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.

By Houze Xu, Jizhong Li, Ziyi Ye

llms nlp robotics fine-tuning multimodal

arXiv AI

Jun 16

From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges

arXiv:2604. 21391v2 Announce Type: replace-cross Abstract: Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal scale mismatch between cognition and action.

By Yiming Zhong, Yaoyu He, Zemin Yang, Pengfei Tian, Yifan Huang, Qingqiu Huang, Xinge Zhu, Yuexin Ma

diffusion robotics safety

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Aug 3

FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution

arXiv:2607. 29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout.

By Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang

diffusion robotics efficiency

arXiv Machine Learning

Jul 17

DriftWorld: Fast World Modeling through Drifting

arXiv:2607. 15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly.

By Susie Lu, Haonan Chen, Weirui Ye, Yilun Du

diffusion robotics benchmarks

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

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