Reconstructing the three-dimensional (3D) geometry of object surfaces is essential for robot perception, yet vision-based approaches are generally unreliable under low illumination or occlusion. This limitation motivates the design of a proprioceptive membrane that conforms to the surface of interest and infers 3D geometry by reconstructing its own deformation. Conventional shape-aware membranes typically rely on resistive, capacitive, or magneto-sensitive mechanisms. However, these methods often encounter challenges such as structural complexity, limited compliance during large-scale deformation, and susceptibility to electromagnetic interference. This work presents a soft, flexible, and stretchable proprioceptive silicone membrane based on optical waveguide sensing. The membrane sensor integrates edge-mounted LEDs and centrally distributed photodiodes (PDs), interconnected via liquid-metal traces embedded within a multilayer elastomeric composite. Rich deformation-dependent light intensity signals are decoded by a data-driven model to recover the membrane geometry as a 3D point cloud. On a customized 140 mm square membrane, real-time reconstruction of large-scale out-of-plane deformation is achieved at 90 Hz with an average reconstruction error of 1.3 mm, measured by Chamfer distance, while maintaining accuracy for indentations up to 25 mm. The proposed framework provides a scalable, robust, and low-profile solution for global shape perception in deformable robotic systems.

翻译：重建物体表面的三维几何形状对于机器人感知至关重要，然而基于视觉的方法在低光照或遮挡条件下通常不可靠。这一局限性促使设计一种能够贴合目标表面并通过重建自身变形来推断三维几何形状的本体感知膜。传统的形状感知膜通常依赖于电阻式、电容式或磁敏机制。然而，这些方法常面临结构复杂性、大尺度变形期间顺应性有限以及易受电磁干扰等挑战。本研究提出了一种基于光波导传感的柔软、柔性且可拉伸的本体感知硅胶膜。该膜传感器集成了边缘安装的LED和中心分布的光电二极管，通过嵌入多层弹性体复合材料内的液态金属迹线相互连接。通过数据驱动模型解码丰富的变形相关光强信号，以三维点云形式恢复膜的几何形状。在定制的140毫米方形膜上，实现了以90赫兹频率实时重建大尺度面外变形，通过Chamfer距离测量的平均重建误差为1.3毫米，同时对于深达25毫米的压痕仍保持精度。所提出的框架为可变形机器人系统中的全局形状感知提供了一种可扩展、鲁棒且低剖面的解决方案。