Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Existing representation-based methods face two limitations: 1) Incomplete Contextual Representation Learning: single-layer or coupled representa...
Xing-Yue Zhao, Wen-Ke Huang, Ling-Hao Zhuang et al.· 0 citations
RynnWorld-4D is introduced, a generative model that co-produces future RGB frames, depth maps, and optical flow from a single RGB-D image and a language instruction within one unified diffusion process, and achieves state-of-the-art performance on real-world dexterous bimanual manipulation tasks, particularly excelling...
Haoyu Zhao, Xingyue Zhao, Siteng Huang et al.· 1 citation
Digital teleoperation, a paradigm that decouples data collection from physical constraints by replacing the real robot with a generative world model, is introduced in RynnWorld-Teleop, a system that integrates depth-aware skeletal conditioning, progressive human-to-robot training on a video Diffusion Transformer, and s...
Haoyu Zhao, Xingyue Zhao, Hangyu Li et al.· 0 citations
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