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    Company Updates CHINGMU Joins OpenLET Community to Co‑Build an Open Ecosystem for Human Motion Data
    Knowledge sharing How to Capture Hand-Object Interaction Data? The Application Value of High-Precision Hand Motion Capture
    Papers sharing A virtual reference trajectory scheme for tracking control of wheeled mobile robots with slip disturbances
    Industry News CHINGMU and JD Group Forge Deep Strategic Partnership to Build Embodied AI Data Collection Ecosystem
    All Company Updates Knowledge sharing Industry News Papers sharing
    CHINGMU Co‑Releases the 2026 China Embodied AI Data Collection Industry Development Blue Paper
    2026/09/08

    CHINGMU Co‑Releases the 2026 China Embodied AI Data Collection Industry Development Blue Paper / Support&Download

    Recently, the 2026 China Embodied AI Data Collection Industry Development Blue Paper, jointly compiled by Zhixing Yuanjie, CHINGMU, and several other organizations, was officially released.
    CHINGMU Joins OpenLET Community to Co‑Build an Open Ecosystem for Human Motion Data
    2026/09/08

    CHINGMU Joins OpenLET Community to Co‑Build an Open Ecosystem for Human Motion Data / Support&Download

    On August 27, CHINGMU Shanghai Vision Technology Co., Ltd. officially joined the OpenLET community.
    CHINGMU:nternational Standard for Robot Performance – How Do You Accurately "Measure" It?
    2026/09/08

    CHINGMU:nternational Standard for Robot Performance – How Do You Accurately "Measure" It? / Support&Download

    Recently, the international standard "Performance evaluation methods of robots for household and similar use" (IEC 62849), developed under China's leadership, was officially published.
    How to Capture Hand-Object Interaction Data? The Application Value of High-Precision Hand Motion Capture
    2026/09/04

    How to Capture Hand-Object Interaction Data? The Application Value of High-Precision Hand Motion Capture / Support&Download

    For robots to operate flexibly in the real-world environment, the first step is not to build more sophisticated models, but to acquire sufficient high-quality real demonstration data. Traditional data-collection methods usually rely on laboratory-based setups, including fixed motion-capture systems, limited worktables and strictly-controlled lighting and spaces. While the captured data delivers high precision, it can hardly be transferred directly to real-world sites such as factories, supermarkets, warehouses and households. Worse still, hand-object interaction involves multi-modal information: hand pose, object pose, contact force and visual feedback. Missing data or time-sequence misalignment in any channel will render the collected demonstrations useless for model training.
    Hot Keywords AI Sports Art Education with AI CG animation Dexterous Hand Synergistic control Humanoid robot
    Popular Article

    CHINGMU Joins OpenLET Community to Co‑Build an Open Ecosystem for Human Motion Data

    On August 27 CHINGMU Shanghai Vision Technolog...

    How to Capture Hand-Object Interaction Data? The Application Value of High-Precision Hand Motion Capture

    For robots to operate flexibly in the real-wor...

    CHINGMU and JD Group Forge Deep Strategic Partnership to Build Embodied AI Data Collection Ecosystem

    Recently CHINGMU and JD Group formally entered...
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