DataLadder: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
2026-06-15 • Robotics
Robotics
AI summaryⓘ
The authors developed DataLadder, a tool that links real robots, simulations, and humans to improve how robot skills are tested and trained. Their system can create accurate virtual copies of robot tasks for easier evaluation and lets humans help make simulated robot movements more natural. It also turns human demonstrations into robot-ready data by checking if movements are physically possible for robots. DataLadder uses a simulator called JoySim and is provided as cloud services, making it easier to generate robot training data and evaluate robot models.
robot policiessimulationdigital twinshuman-robot interactiondata generationmodel evaluationJoySimcloud servicesrobot trajectoriesphysical constraints
Authors
Peidong Liu, Yongce Liu, Songyan Guo, Fuyuan Ma, Zhihao Yuan, Ao Li, Zengjue Chen, Wenhao Li, Tianle Zhang, Mingyang Li, Jiale Zhang, Junzhe Xiong, Zhiyuan Xiang, Dafeng Chi, Yuzheng Zhuang, Yihang Li, Qingrong He, Jiaming Liang, Chen Cai, Peng Hao, Mingxi Luo, Song Wang, Junwu Xiong, Ruodai Li, Liyi Luo, Wei Tan, Dongjiang Li, Jiawei Li, Hui Shen, Yicheng Gong, Liang Lin
Abstract
Generalist robot policies require trustworthy evaluation and robot-usable training data, but both are difficult to scale with physical robots alone. Real-robot trials and demonstrations remain the most faithful source of deployment signals, yet they are slow, costly, and hard to reproduce. We present DataLadder, a simulation-enabled interconversion toolchain for human-robot aligned model evaluation and data generation, denoted as Robot $\rightleftharpoons$ Simulation $\rightleftharpoons$ Human. On the one hand, the Robot $\rightarrow$ Simulation $\rightarrow$ Human pathway supports human-robot aligned model evaluation by reconstructing real-robot tabletop organization tasks as calibrated digital twins for scalable evaluation, while using human embodied feedback to inspect and refine the naturalness of simulated motions. On the other hand, the Human $\rightarrow$ Simulation $\rightarrow$ Robot pathway supports human-robot aligned data generation: it lifts ego-centric human demonstrations into simulation, checks them under robot physical constraints, and converts them into robot-centered trajectories, annotations, and visual observations. Together, these pathways use the JoySim simulator as both a scalable evaluation layer and a physical consistency filter for robot data generation. We further package the core reconstruction, simulation, rendering, and realism-augmentation modules as cloud services on JD Cloud, turning the system into reusable infrastructure for robot data generation and model evaluation.