Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots

• URL: http://arxiv.org/abs/2605.12790v1
• Date: Tue, 12 May 2026 22:07:53 GMT
• Title: Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots
• Abstract summary: We present a physics-informed neural network (PINN) for kinematic modeling of a 6-DoF CTR with three pre-curved tubes.<n>PINN enables full-state estimation of shape, twist angle, torsional strain, bending moment, and orientation.<n>The resulting model is also computationally efficient and robust, making it well-suited for real-time control applications.
• Score: 4.539331900295919
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modeling concentric tube robots (CTRs) involves complex nonlinear continuum mechanics, and despite recent progress, physics-based models often lack an accurate representation of the experimental setups. To overcome these limitations, deep neural network-based models have been explored as alternatives with superior accuracy; however, they often overlook known mechanics, require large training datasets, and typically discard shape estimation of the robot. We present a physics-informed neural network (PINN) for kinematic modeling of a 6-DoF CTR with three pre-curved tubes that embeds the Cosserat rod differential equations and learns from few-shot observational data, balancing physics priors with data-driven fitting. PINN enables full-state estimation of shape, twist angle, torsional strain, bending moment, and orientation. Benchmark tests show a mean shape error below 1% of the robot length and accurately recovered other kinematic states, outperforming a purely physics-based Cosserat rod model baseline while using a minimal training set. The resulting model is also computationally efficient and robust, making it well-suited for real-time control applications.

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