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20222026
most citedUnimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

5 citations · 17 across the 41 of their papers we have counts for

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Showing 2025 · cs.ROShow all

6 papers · 2 filters

cs.RO2025

Video2Act: A Dual-System Video Diffusion Policy with Robotic Spatio-Motional Modeling

Yueru Jia, Jiaming Liu, Shengbang Liu +7

Robust perception and dynamics modeling are fundamental to real-world robotic policy learning. Recent methods employ video diffusion models (VDMs) to enhance robotic policies, impr…

cs.RO2025

WoW: Towards a World omniscient World model Through Embodied Interaction

Xiaowei Chi, Peidong Jia, Chun-Kai Fan +33

Humans develop an understanding of intuitive physics through active interaction with the world. This approach is in stark contrast to current video models, such as Sora, which rely…

cs.RO2025

ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory

Ying Li, Xiaobao Wei, Xiaowei Chi +7

Data scarcity continues to be a major challenge in the field of robotic manipulation. Although diffusion models provide a promising solution for generating robotic manipulation vid…

cs.RO2025

MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis

Xiaowei Chi, Kuangzhi Ge, Jiaming Liu +9

Video Generation Models (VGMs) have become powerful backbones for Vision-Language-Action (VLA) models, leveraging large-scale pretraining for robust dynamics modeling. However, cur…

cs.RO2025

ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance

Ying Li, Xiaobao Wei, Xiaowei Chi +6

While recent advancements in robotic manipulation video synthesis have shown promise, significant challenges persist in ensuring effective instruction-following and achieving high…

cs.RO2025

MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation

Rongyu Zhang, Menghang Dong, Yuan Zhang +6

Multimodal Large Language Models (MLLMs) excel in understanding complex language and visual data, enabling generalist robotic systems to interpret instructions and perform embodied…