Paper Detail
Yifan Ye, Yankai Fu, Yaoxu Lv, Bohan Hou, Jun Cen, Lingdong Kong, Duo Zheng, Tianxing Chen, Jiaming Liu, Ziang Cao, Yunfan Lou, Wei Chow, Xian Sun, Yingshuo Wang, Kuangzhi Ge, Xiaowei Chi, Xidong Zhang, Zhibo Pang, Yiwu Zhong, Sirui Han, Zhihe Lu, Weihao Yuan, Qifeng Chen, Michael Yu Wang, Yao Mu, Ziwei Liu, Jianfei Yang, Ping Luo, Shanghang Zhang
Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations with physical states and actions. These signals can be provided, to varying degrees, by multiple data sources. In this work, we organize the embodied data ecosystem as a "pyramid" spanning five complementary sources: real-robot data, UMI-style data, egocentric and exocentric data, simulation data, and general vision-language data. We organize the pyramid around the tension between scalability and robot alignment, and further characterize each source in terms of data quality, diversity, reusability, and physical fidelity. We then analyze recent embodied foundation models through the lens of their data recipes, examining how different sources are selected, aligned, and mixed during pretraining. For embodied brain models, vision-language-action models, and world-action models alike, we relate data composition to capabilities in perception, reasoning, planning, action generation, and world prediction. We close by discussing six open challenges: building large-scale tactile datasets, collecting failure and recovery data, developing scalable data-collection pipelines, aligning actions across embodiments, leveraging egocentric data for dexterous manipulation, and designing principled data recipes for robot learning. We hope this work paves the foundation for the design of next-generation embodied systems.
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@article{ye2026data,
title = {Data Pyramid for Embodied Manipulation},
author = {Yifan Ye and Yankai Fu and Yaoxu Lv and Bohan Hou and Jun Cen and Lingdong Kong and Duo Zheng and Tianxing Chen and Jiaming Liu and Ziang Cao and Yunfan Lou and Wei Chow and Xian Sun and Yingshuo Wang and Kuangzhi Ge and Xiaowei Chi and Xidong Zhang and Zhibo Pang and Yiwu Zhong and Sirui Han and Zhihe Lu and Weihao Yuan and Qifeng Chen and Michael Yu Wang and Yao Mu and Ziwei Liu and Jianfei Yang and Ping Luo and Shanghang Zhang},
year = {2026},
abstract = {Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations with physical states and actions. These signals can be provided, to varying degrees, by multiple data sources. In this work, we organize the embodied data ecosystem as a "pyramid" spanning five complementary sources: real-robot data, UMI-style data, egocentric and exocentric data, simulation data, and general vision-lan},
url = {https://arxiv.org/abs/2607.24744},
keywords = {cs.RO, cs.CV},
eprint = {2607.24744},
archiveprefix = {arXiv},
}
{}