Highly Deformable Proprioceptive Membrane for Real-Time 3D Shape Reconstruction

TL;DR

This paper introduces a soft, optical waveguide-based proprioceptive membrane that enables real-time 3D shape reconstruction of deformable surfaces, enhancing robotic perception under challenging conditions.

Contribution

It presents a novel stretchable silicone membrane with integrated optical sensors and a data-driven decoding model for accurate, high-speed 3D shape sensing of large deformations.

Findings

Achieves 90 Hz real-time 3D reconstruction

Average error of 1.3 mm in shape estimation

Maintains accuracy for indentations up to 25 mm

Abstract

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.