无需点对应关系，用神经网络直接学习软表面形变映射。
Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control
- 用神经网络同时学习形变函数与置信度图，实现无对应关系的仿真到现实迁移。
- 支持3D扫描点云或有缺失标记点的运动捕捉数据输入，鲁棒性强。
- 适用于气动软体机器人、可变形膜和人形机器人等多场景控制任务。
本文提出一种无对应关系、基于函数的仿真到现实学习方法，用于控制可变形自由曲面。不同于依赖完整标记点对应的传统方法，本方法通过神经网络联合学习形变函数空间与置信度图，将仿真形状映射到真实世界形态。该方法可接受3D扫描生成的点云（无对应关系）或运动捕捉系统的标记点（容忍丢失标记），并可无缝集成至基于神经网络的逆运动学与形状控制计算流程中。我们在四种气动驱动的软体机器人上验证了该方法的通用性与适应性：可变形膜、机器人模特及两个软体夹持器。
原文摘要 · Abstract (English)
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.
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