Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control

doi:10.1109/TRO.2025.3647769

Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control

2026-01-01

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DOI：10.1109/TRO.2025.3647769

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This article presents a correspondence-free, function-based sim-to-real learning method for controlling deformable freeform surfaces. Unlike traditional sim-to-real transfer methods that strongly rely on marker points with full correspondences, our approach simultaneously learns a deformation function space and a confidence map—both parameterized by a neural network (NN)—to map simulated shapes to their real-world counterparts. As a result, the sim-to-real learning can be conducted by input from either a 3-D scanner as point clouds (without correspondences) or a motion capture system as marker points (tolerating missed markers). The resultant sim-to-real transfer can be seamlessly integrated into a NN-based computational pipeline for inverse kinematics and shape control. We demonstrate the versatility and adaptability of our method on two vision devices and across four pneumatically actuated soft robots: a deformable membrane, a robotic mannequin, and two soft manipulators.

Keywords:

Correspondence-free

deformation control

free-form surface

sim-to-real learning

soft robotics

Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control

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