Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

By Youcan Yan, Zhe Hu, Zhengbao Yang, Wenzhen Yuan, Chaoyang Song, Jia Pan, Yajing Shen. Science Robotics, 2021

514 citations recorded by the source corpus.

Flexible magnetic sensors have tactile hyperacuity and the ability to distinguish between normal and shear forces. Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0d33b9e654a5e941cb119224ddb72d7b055b2be7

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Citations in the corpus

ReSkin: versatile, replaceable, lasting tactile skins (2021, background): “Machine learning approaches have recently been shown to effectively parse soft sensor data for robotic grasping [7, 8, 9], proprioception [10], and object classification [11], among many others [12, 13, 14].”