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General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie supervision to task-internal anchors such as preferences or normalized progress, none of...
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...
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...
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