Soft manipulationExpert
Learning a Resolution-Consistent Jacobian Field for Bio-Inspired Rigid-Soft Finger
This preprint introduces Jacobian Flow Matching (JFM), a Conditional Flow Matching-based learned Jacobian field for a tendon-driven rigid-soft finger. On real finger data, it cuts global average single-step RMSE by over 53% versus a pointwise baseline and improves sparse-sampling trajectory fidelity, reducing median RMSE by 14.43% at stride 8. It matters because it gives compliant dexterous hands a resolution-consistent local model usable for both prediction and trajectory optimization.