Magnetic Tactile-Driven Soft Actuator for Intelligent Grasping and Firmness Evaluation
Chengjin Du, Federico Bernabei, Zhengyin Du, Sergio Decherchi, Matteo Lo Preti, Lucia Beccai

TL;DR
This paper introduces the SoftMag soft actuator with integrated magnetic tactile sensing and a neural network-based decoupling method, enabling precise grasping and firmness evaluation of delicate objects in real-time.
Contribution
It presents the first unified magnetic tactile-sensorized soft actuator with a neural decoupling strategy to improve sensing accuracy amidst actuator deformations.
Findings
Successful decoupling of tactile signals from actuator deformation.
Real-time prediction of contact forces and object firmness.
High correlation (Pearson r > 0.8) between estimated and actual firmness.
Abstract
Soft robots are powerful tools for manipulating delicate objects, yet their adoption is hindered by two gaps: the lack of integrated tactile sensing and sensor signal distortion caused by actuator deformations. This paper addresses these challenges by introducing the SoftMag actuator: a magnetic tactile-sensorized soft actuator. Unlike systems relying on attached sensors or treating sensing and actuation separately, SoftMag unifies them through a shared architecture while confronting the mechanical parasitic effect, where deformations corrupt tactile signals. A multiphysics simulation framework models this coupling, and a neural-network-based decoupling strategy removes the parasitic component, restoring sensing fidelity. Experiments including indentation, quasi-static and step actuation, and fatigue tests validate the actuator's performance and decoupling effectiveness. Building upon…
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Taxonomy
TopicsSoft Robotics and Applications · Advanced Sensor and Energy Harvesting Materials · Robot Manipulation and Learning
