A Super-Resolution and Multi-Axis Tactile Sensor with Soft Artificial Skin

Venue: RSS 2026 (Sydney, Jul 13–17) · Session: Robot & Sensor Design · paper #199 Authors: Hongxu Wei, Zhoulu, Peisen Xu, Yang Xu, Xuanyi Yang, Wei Zhao, Jiming Chen, Gaofeng Li Program page: https://roboticsconference.org/program/papers/199/

No public preprint found (searched arXiv, Aug 6 2026); summary derived from the verified program abstract. Trend placement and neighbors: RSS 2026 survey.

Summary

Problem. Adding a soft layer over a sensing array is a popular route to human-like super-resolution tactile skin, but most existing sensing units can measure only normal force, whereas manipulation needs multi-dimensional force information. Method. The paper proposes a tactile sensing unit built on a tiny monolithic tri-cantilever structure that decouples three-dimensional force, with a hybrid model-based + learning-based reconstruction algorithm; units are arrayed and covered with a soft silicone layer whose traction-coupling effects, exploited by deep learning, enable super-resolved estimation of 3D force magnitude and contact position. Results. The sensor achieves 0.19 N MAE for three-dimensional force estimation and 0.49 mm for contact localization — a 26-fold spatial-resolution improvement the authors state surpasses the state of the art — and is validated in teleoperated test-tube-into-rack transfer and stable grasping under external interference.

Abstract

To achieve human-like skin tactile perception with super-resolution, the method of introducing a soft layer on sensing array has attracted increasing attention. Due to the limitations of sensing units principle, most existing tactile sensors can only sense normal force. However, multi-dimensional force information is important for robot manipulation. To address this, we propose a tactile sensing unit based on a tiny monolithic tri-cantilever structure that decouples three-dimensional force. A reconstruction algorithm combined both model-based and learning-based approaches is then proposed to detect the three-dimensional force applied to the sensing unit. These units are arranged in an array and covered with a soft silicone layer which induces traction-coupling effects. By leveraging deep learning, our tactile sensor can estimate the magnitude and position of external three-dimensional force with super-resolution. Experiments have shown that our tactile sensor achieves a Mean Absolute Error (MAE) of 0.19,N for three-dimensional force estimation and 0.49,mm for contact localization. Notably, this corresponds to a 26-fold improvement in spatial resolution, surpassing the state-of-the-art literature. Then the benefits and potential applications of our proposed sensors are validated in several tasks, including the teleoperative transfer of a test tube into a rack and stable robotic grasping under external interference. These demonstrate the practicality of our design and provide new solutions for tactile sensors.

Wiki context

Related topic reviews: Review-Tactile-VLA · Review-Dexterous-Manipulation

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