製造可能なセンサ配置と変形固有感覚のモデルフリー共最適化
Model-Free Co-Optimization of Manufacturable Sensor Layouts and Deformation Proprioception
柔軟センサの数・長さ・配置と形状予測ネットワークを、物理モデルなしでデータのみから同時最適化し、大変形の固有感覚精度を向上させる手法を提案。
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著者: Yingjun Tian, Guoxin Fang, Aoran Lyu, Xilong Wang, Zikang Shi, Yuhu Guo, Weiming Wang, Charlie C. L. Wang
分類: cs.RO
原文アブストラクト
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.
