Robotics: Science and Systems XXII

LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion

Jiangran Lyu, Kai Liu, Xuheng Zhang, Haoran Liao, Yusen Feng, Wenxuan Zhu, Tingrui Shen, Jiayi Chen, Jiazhao Zhang, Yifei Dong, Cui Wenbo, Senmao Qi, Shuo Wang, Yixin Zheng, Mi Yan, Xuesong Shi, Haoran Li, Dongbin Zhao, Ming-Yu Liu, Zhizheng Zhang, Li Yi, Yizhou Wang, He Wang

Abstract:

Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous embodied data. While the Unified World Model (UWM) formulation has the potential to leverage such diverse data, existing instantiations struggle to scale to foundation-level due to coarse data usage and fragmented datasets. We introduce LDA-1B, a robot foundation model that scales through universal embodied data ingestion by jointly learning dynamics, policy, and visual forecasting, assigning distinct roles to data of varying quality. To support this regime at scale, we assemble and standardize EI-30k, an embodied interaction dataset comprising over 30k hours of human and robot trajectories in a unified format. Scalable dynamics learning over such heterogeneous data is enabled by operating in a structured DINO latent space, which avoids redundant pixel-space appearance modeling. Complementing this representation, LDA-1B employs a mixed-frequency multi-modal diffusion transformer to handle asynchronous vision and action streams, enabling stable training at the 1B-parameter scale. Experiments in simulation and the real world show LDA-1B outperforms prior methods (e.g., π_{0.5}) by up to 21%, 48%, and 23% on contact-rich, dexterous, and long-horizon tasks, respectively. Notably, LDA-1B enables data-efficient fine-tuning, gaining 10% by leveraging 30% low-quality trajectories typically harmful and discarded. The code and data will be publicly released to benefit the community.

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Bibtex:

  
@INPROCEEDINGS{LyuJ-RSS-26, 
    AUTHOR    = {Jiangran Lyu AND Kai Liu AND Xuheng Zhang AND Haoran Liao AND Yusen Feng AND Wenxuan Zhu AND Tingrui Shen AND Jiayi Chen AND Jiazhao Zhang AND Yifei Dong AND Cui Wenbo AND Senmao Qi AND Shuo Wang AND Yixin Zheng AND Mi Yan AND Xuesong Shi AND Haoran Li AND Dongbin Zhao AND Ming-Yu Liu AND Zhizheng Zhang AND Li Yi AND Yizhou Wang AND He Wang}, 
    TITLE     = {{LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.210} 
}