MagPillar: A Soft Magnetopillar Array Tactile Sensor for Robotic Surface Perception
MagPillar: A Soft Magnetopillar Array Tactile Sensor for Robotic Surface Perception
This paper introduces MagPillar, a tactile sensor inspired by spider hairs that combines a magnetic pillar array with three-axis Hall sensing. The authors provide a full-array mechanical and magnetic-field model to interpret contact responses, demonstrating an offline accuracy of 99.34% for distinguishing six material surfaces under within-session temporally blocked cross-validation. Continuous scans over periodic protrusions show consistent amplitude and peak-spacing variations reflecting surface geometry changes.
The authors present MagPillar, a tactile sensor integrating magnetic pillars with three-axis Hall sensing to achieve high-accuracy material classification and surface geometry detection in robotic experiments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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n affects the superposed fields measured across the array, providing a physical basis for interpreting its contact responses. The compliant pillar architecture translates contact with surface textures and geometric features into distinct magnetic response patterns. Then, in robotic surface perception experiments, spectral features and logistic regression distinguish six material surfaces with an offline accuracy of 9
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9.34% under within-session temporally blocked cross-validation. Continuous scans over periodic protrusions produce amplitude and peak-spacing variations consistent with changes in surface geometry. These findings connect compliant contact mechanics with distributed magnetic sensing, paving a new path for material recognition and qualitative surface-profile perception.
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Source：arXiv Robotics — research abstracts · arxiv.org