ForeTac-VLA is proposed, a forecasting-based tactile-vision-language fusion model that predicts future tactile states to guide action generation and achieves an average success rate of 95%, outperforming the fine-tuned VLA model by 36.25 percentage points and state-of-the-art tactile-enhanced VLA baselines by over 22 p...
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...
Dongchi Huang, Hong-Yin Zhang, Bo-Han Hou et al.· 7 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
