Model-Free Co-Optimization of Manufacturable Sensor Layouts and Deformation Proprioception

2026-03-09

AI Summary

This work addresses the challenge of suboptimal strain sensing accuracy in soft systems due to sensor layouts that are often designed through empirical rules or trial-and-error. To overcome this limitation, we propose a model-free, data-driven end-to-end joint optimization framework that simultaneously optimizes the number, length, placement of flexible length-measurement sensors, and the parameters of the deformation prediction network—without requiring any physical simulation model. Our approach is the first to enable co-optimization of sensor layout and prediction network, while explicitly incorporating manufacturability constraints to balance practical deployability with high accuracy. Experiments across diverse soft robotic and wearable platforms demonstrate that the proposed framework significantly improves large-deformation prediction accuracy, confirming its effectiveness and generalizability.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Flexible sensors are increasingly employed in soft robotics and wearable devices to provide proprioception of freeform deformations.Although supervised learning can train shape predictors from sensor signals, prediction accuracy strongly depends on sensor layout, which is typically determined heuristically or through trial-and-error. This work introduces a model-free, data-driven computational pipeline that jointly optimizes the number, length, and placement of flexible length-measurement sensors together with the parameters of a shape prediction network for large freeform deformations. Unlike model-based approaches, the proposed method relies solely on datasets of deformed shapes, without requiring physical simulation models, and is therefore broadly applicable to diverse robotic sensing tasks. The pipeline incorporates differentiable loss functions that account for both prediction accuracy and manufacturability constraints. By co-optimizing sensor layouts and network parameters, the method significantly improves deformation prediction accuracy over unoptimized layouts while ensuring practical feasibility. The effectiveness and generality of the approach are validated through numerical and physical experiments on multiple soft robotic and wearable systems.

Problem

• sensor layout optimization
• deformation proprioception
• soft robotics
• wearable devices
• model-free optimization

Innovation

• model-free co-optimization
• flexible sensor layout
• deformation proprioception
• differentiable optimization
• data-driven sensing