Hugging Face Trending Papers July 27, 2026

DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning

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Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.

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arXiv Machine Learning

Jun 10

GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation

arXiv:2606. 10025v1 Announce Type: cross Abstract: We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution.

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arXiv AI

Jun 2

From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data

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robotics multimodal

arXiv Machine Learning

Jun 16

Geometric Action Model for Robot Policy Learning

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arXiv Computer Vision

Sep 22

AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation

arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...

By Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen

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Hugging Face Trending Papers

Jul 29

ContactFlow: A video action conditioning that transfers across embodiments

World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact.

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arXiv AI

Jun 11

LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition

arXiv:2606. 11628v1 Announce Type: cross Abstract: The most widely-adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments.

By Harsh Gupta, Guanya Shi, Wenzhen Yuan

robotics