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Deep learning–enabled versatile shape perception for soft robots via single-ended multimode fiber

delete2026-06-12
delete0
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OA
AI
Z
Zhaofan He
L
Lele Wang
H
Haidi Geng
Z
Zhengyang Lu
T
Tiantian He
H
Hongkun Zhong
H
Hailong Zhang
R
Runfeng Zhu
Q
Qingxiang Zhao
孟
孟元 (Yuan Meng)
D
Dan Li
P
Ping Yan
Q
Qiang Liu *
Q
Qirong Xiao *
DOI：10.1126/sciadv.aef6263delete
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摘要

摘要

En 中文
软体机器人向具身智能系统的演化从根本上依赖于精确的本体感知。然而，一种通用的解决方案来捕获多样化交互过程中的连续变形，尤其是在空间受限的介入场景中，仍然缺乏。在此，我们介绍一种基于单端多模光纤（MMF）的深度学习赋能的通用形状感知方法。通过利用光学的固有集成优势，我们极简的反射式架构物理上消除了对复杂解调单元和远程设备的依赖。此外，将混沌光学散斑场视为编码高维形状信息的数据流，可重构神经解码器将单个物理通道解析为适用于异构任务的通用感知模式：软体夹持器的离散状态确认（>99%准确率）、仿生灵巧手的连续形状跟踪（约5倍空间分辨率提升）以及软体手术机器人的直观三维形态重建（IoU>0.93）。总体而言，我们的工作建立了一个通过计算突破硬件适应性限制的通用框架，为软体机器人的数字孪生闭环控制奠定了坚实基础。

期刊

Science Advances 封面图
Science Advances
IF:
12.5
论文数:
2.1W
被引数:
18.1W

机构

T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
S
sichuan university
学者数:
12.1W
论文数: 7.8W
被引数: 100
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