A Flow Matching Framework for Soft-Robot Inverse Dynamics

• URL: http://arxiv.org/abs/2604.03006v1
• Date: Fri, 03 Apr 2026 12:45:46 GMT
• Title: A Flow Matching Framework for Soft-Robot Inverse Dynamics
• Abstract summary: inverse-dynamics framework for open-loop feedforward control learns the system's differential dynamics as a generative transport map.<n>Work demonstrates flow matching as a robust, high-performance paradigm for learning differential inverse dynamics in soft robotic systems.
• Score: 13.045225329973617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning the inverse dynamics of soft continuum robots remains challenging due to high-dimensional nonlinearities and complex actuation coupling. Conventional feedback-based controllers often suffer from control chattering due to corrective oscillations, while deterministic regression-based learners struggle to capture the complex nonlinear mappings required for accurate dynamic tracking. Motivated by these limitations, we propose an inverse-dynamics framework for open-loop feedforward control that learns the system's differential dynamics as a generative transport map. Specifically, inverse dynamics is reformulated as a conditional flow-matching problem, and Rectified Flow (RF) is adopted as a lightweight instance to generate physically consistent control inputs rather than conditional averages. Two variants are introduced to further enhance physical consistency: RF-Physical, utilizing a physics-based prior for residual modeling; and RF-FWD, integrating a forward-dynamics consistency loss during flow matching. Extensive evaluations demonstrate that our framework reduces trajectory tracking RMSE by over 50% compared to standard regression baselines (MLP, LSTM, Transformer). The system sustains stable open-loop execution at a peak end-effector velocity of 1.14 m/s with sub-millisecond inference latency (0.995 ms). This work demonstrates flow matching as a robust, high-performance paradigm for learning differential inverse dynamics in soft robotic systems.

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