{"ID":2878690,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00060","arxiv_id":"2509.00060","title":"Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control","abstract":"This paper 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 -- 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 3D 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 neural network-based computational pipeline for inverse kinematics and shape control. We demonstrate the versatility and adaptability of our method on both vision devices and across four pneumatically actuated soft robots: a deformable membrane, a robotic mannequin, and two soft manipulators.","short_abstract":"This paper 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 confide...","url_abs":"https://arxiv.org/abs/2509.00060","url_pdf":"https://arxiv.org/pdf/2509.00060v3","authors":"[\"Yingjun Tian\",\"Guoxin Fang\",\"Renbo Su\",\"Aoran Lyu\",\"Neelotpal Dutta\",\"Weiming Wang\",\"Simeon Gill\",\"Andrew Weightman\",\"Charlie C. L. Wang\"]","published":"2025-08-25T17:54:24Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}