[
    {
        "id": "osp-25463",
        "type": "article-journal",
        "title": "Learning a Resolution-Consistent Jacobian Field for Bio-Inspired Rigid-Soft Finger",
        "author": [
            {
                "family": "Liang",
                "given": "Tianyou"
            },
            {
                "family": "Zeng",
                "given": "Haisen"
            },
            {
                "family": "Chen",
                "given": "Shanjun"
            },
            {
                "family": "Zhu",
                "given": "Yiming"
            },
            {
                "family": "Lu",
                "given": "Zhongyue"
            },
            {
                "family": "Luo",
                "given": "Zirong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/learning-a-resolution-consistent-jacobian-field-for-bio-inspired-rigid-soft-finger",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Bio-inspired tendon-driven rigid-soft coupled dexterous fingers exhibit strong nonlinearity and configuration-dependent sensitivity, making accurate modeling challenging. In discrete-time control, Jacobian-based kinematic algorithms typically rely on point-wise local linear approximations, which makes their performance sensitive to sensor sampling frequency and controller update frequency. To address this issue, we propose Jacobian Flow Matching (JFM), a structured learning framework based on Conditional Flow Matching (CFM), to learn a resolution-consistent Jacobian field that models actuation-to-motion transitions as a dynamical flow. The proposed framework supports both single-step prediction and continuous rollout via ODE integration, enabling consistent inference across temporal resolutions. Experiments on a tendon-driven rigid-soft finger show that the proposed method suppresses outlier errors and improves single-step prediction accuracy, reducing the global average RMSE by over 53% compared with a baseline discrete Jacobian learning approach. For long-horizon prediction, trajectories recovered via ODE integration achieve higher fidelity under sparse sampling (Stride = 8), reducing the RMSE median by 14.43% and the error variance by 24.87%. These results demonstrate that the learned flow-based Jacobian field provides an effective local model for offline multi-step trajectory optimization in rigid-soft coupled nonlinear systems."
    }
]