Learning 3D Shape Proprioception for Continuum Soft Robots with Multiple Magnetic Sensors

T.A. Baaij, Marn Klein Holkenborg, Maximilian Stölzle*, Daan van der Tuin, Jonatan Naaktgeboren, Robert Babuska, Cosimo Della Santina

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Sensing the shape of continuum soft robots without obstructing their movements and modifying their natural softness requires innovative solutions. This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception. Magnetic sensors are compact, sensitive, and easy to integrate into a soft robot. We also propose a neural architecture to make sense of the highly nonlinear relationship between the perceived intensity of the magnetic field and the shape of the robot. By injecting a priori knowledge from the kinematic model, we obtain an effective yet data-efficient learning strategy. We first demonstrate in simulation the value of this kinematic prior by investigating the proprioception behavior when varying the sensor configuration, which does not require us to re-train the neural network. We validate our approach in experiments involving one soft segment containing a cylindrical magnet and three magnetoresistive sensors. During the experiments, we achieve mean relative errors of 4.5%.
Original languageEnglish
Pages (from-to)44-56
JournalSoft Matter
Issue number1
Publication statusPublished - 2022

Bibliographical note

Special issue on "Advanced Materials and Processes for Soft Robotics"


This work has received funding under the European Union’s Horizon Europe Programme from Project EMERGE - Grant Agreement No. 101070918.


  • Soft Robotics


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