Shape-morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human-machine interfaces, biomimetic robotics, and tools for interacting with biological systems. To achieve three-dimensional (3D) programmable shape morphing (PSM), the deployment of array-based actuators is essential. However, a critical knowledge gap impeding the development of 3D PSM is the challenge of controlling the complex systems formed by these soft actuator arrays. This study introduces a novel approach, for the first time, representing the configuration of shape morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. We propose Shape Morphing Net (SMNet), a method that realizes the regression from point cloud data to high-dimensional continuous vectors. Applied to previous 2D PSM actuator arrays, SMNet significantly enhances control precision from 82.23% to 97.68%. Further, we extend its application to 3D PSM devices with three different actuator mechanisms, demonstrating the universal applicability of SMNet to the control of 3D shape morphing technologies. In our demonstrations, we confirm the efficacy of inverse control, where 3D PSM devices successfully replicate target shapes. These shapes are obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable. The findings of this research represent a substantial advancement in soft robotics, particularly for applications demanding intricate 3D shape transformations, and establish a foundational framework for future developments in the field.

翻译：形状变形装置作为软体机器人领域的重要分支，在人机交互界面、仿生机器人及生物系统交互工具等领域具有重要应用价值。为实现可编程三维形状变形，必须部署阵列式驱动单元。然而，阻碍三维可编程形状变形发展的关键知识瓶颈在于如何控制由软体驱动器阵列构成的复杂系统。本研究首次提出一种创新方法，采用点云数据表征形状变形装置的构型，并利用深度学习将这些构型映射至控制输入。我们提出形状变形网络（SMNet）方法，实现从点云数据到高维连续向量的回归映射。将其应用于已有的二维可编程形状变形驱动器阵列后，控制精度从82.23%显著提升至97.68%。进一步地，我们将该方法扩展至三种不同驱动机制的三维可编程形状变形装置，验证了SMNet对三维形状变形技术控制的普适性。在示范实验中，我们证实了逆向控制的有效性——三维可编程形状变形装置成功复现目标形状。这些形状既可通过物理实体三维扫描获取，亦可借助三维建模软件生成。结果表明，在三维可编程形状变形装置的可形变范围内，能够准确复现期望形状。本研究成果标志着软体机器人领域的重要进展，尤其适用于需要复杂三维形状变换的应用场景，并为该领域的未来发展奠定了框架基础。