cs.ROOct 7, 2026

MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

Authors: Yu Feng, Hao Wu, Haotian Guo, Haoming Liu, William Su, Jingxiang Guo, Jiankun Li, Masayoshi Tomizuka, +2 more

Organizations: Department of Mechanical Engineering, National University of Singapore, Singapore · Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA · Mechanical Engineering Department, City University of Hong Kong, Kowloon, Hong Kong SAR, and CAS-CityU Joint Laboratory for Robotic Research, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

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Abstract

Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained through contact. Here, we present a compact magnetociliary tactile sensor (MagCilia) that combines a flexible magnetic-cilia structure with a Hall sensor for 3D force sensing. Quasi-static finite element analysis is used to investigate structural deformation and magnetic responses under multidirectional loading. To reconstruct forces from the coupled magnetic channels, we propose causal history fusion regression (CHFR), which combines current magnetic-field measurements with their recent changes. Five-fold cross-validation grouped by calibration record yields root-mean-square errors of 0.40, 0.57, and 0.69 N for Fx, Fy, and Fz, respectively, with corresponding coefficients of determination of 0.93, 0.90, and 0.92. Robotic experiments demonstrate tangential-force-guided gripper adjustment and multi-axis load monitoring under external perturbations. Frequency-domain features of the reconstructed forces distinguish six surface categories with 99.39% accuracy in three-fold cross-validation grouped by acquisition session. An online robotic demonstration additionally identifies all six tested surfaces. These results demonstrate 3D force reconstruction, grasping feedback, and surface recognition using a single compact tactile unit.

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Fig. 1: Photograph and layered structure of MagCilia. (a) Sensor prototype with a scale reference; (b) magnetic-cilia skin, buffer layer, readout circuit board, and housing.
Fig. 2: Contact sensing mechanism of MagCilia. Normal, x/yx/y tangential, and oblique loading induce different deformations of the magnetic cilia and supporting structure.
Fig. 3: Quasi-static deformation and normalized magnetic response. (a) Normal indentation and corresponding magnetic response; (b) xx shear deformation (left) and magnetic response under orthogonal shear (right); (c) yy shear deformation (left) and magnetic response under oblique shear (right).
Fig. 4: 3D force calibration setup and data acquisition interface. (a) Sensor installation and contact loading; (b) 3D reference force, magnetic readout, and synchronized recording.
Fig. 5: Causal history fusion regression (CHFR) model for 3D force reconstruction.
Fig. 6: 3D force reconstruction results. (a) Coefficient of determination for each force component; (b) absolute-error distributions; (c) scatter plots of reference forces versus out-of-fold predictions with error metrics.
Fig. 7: Dynamic-touch response. (a) Fingertip loading and reference-sensor setup; (b-e) reference forces (red dashed) and predictions (blue solid) during contacts with varying magnitudes, directions, and repetition rates.
Fig. 8: Force-guided robotic grasping with MagCilia. (a) Grasping, perturbation, loading, and object-removal stages; (b) force-feedback and state logic for adjusting the gripper opening; (c) corresponding CHFR-reconstructed 3D forces.
Fig. 9: Stepwise loading during grasping. (a) Images of the three loading steps; (b) corresponding time series of the CHFR-reconstructed 3D forces.
Fig. 10: Lateral and vertical perturbations during grasping. (a) Perturbation actions; (b) CHFR-reconstructed 3D forces and corresponding perturbation intervals.
Fig. 11: Robotic surface scanning and recognition. (a) Robotic scanning setup with MagCilia; (b) 3D force frequency-band features and classification pipeline; (c) real-time robotic recognition demonstration for six surface categories; (d) row-normalized confusion matrix from three-fold session-grouped offline evaluation, with an overall accuracy of 99.39%.
SensorConfigurationCapabilityQuantitative performance
[ 17 ]Magnetized film with Hall sensingNormal and 1D tangential forceSensitivity: 0.01 kPa -1 (normal), 0.10–0.27 kPa -1 (tangential)
[ 18 ]Stacked magnetic films with a 3×33\times 3 Hall array3D force sensingRange: 28 N (normal), ±8\pm 8 N (tangential)
[ 20 ]Magnetic-cilia array with a 2×42\times 4 sensor arrayTangential force and directionResolution: 0.2 mN
[ 21 ]Soft-magnetic cilia with an induction coilWeak tangential force sensingSensitivity: 10.03%/mN
[ 22 ]Segmented magnetic ciliaFlow and texture sensingFlow-direction error: <10∘<10^{\circ}
[ 23 ]Magnetic cilia with a single Hall sensorNormal force and tangential directionNormal sensitivity: 0.02 N -1 ; R2=0.97R^{2}=0.97
TABLE I: Comparison of MagCilia with representative magnetic tactile sensors.
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