FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network
arXiv:1810.02653
Abstract
Tactile sensing is essential to the human perception system, so as to robot. In this paper, we develop a novel optical-based tactile sensor "FingerVision" with effective signal processing algorithms. This sensor is composed of soft skin with embedded marker array bonded to rigid frame, and a web camera with a fisheye lens. While being excited with contact force, the camera tracks the movements of markers and deformation field is obtained. Compared to existing tactile sensors, our sensor features compact footprint, high resolution, and ease of fabrication. Besides, utilizing the deformation field estimation, we propose a slip classification framework based on convolution Long Short Term Memory (convolutional LSTM) networks. The data collection process takes advantage of the human sense of slip, during which human hand holds 12 daily objects, interacts with sensor skin and labels data with a slip or non-slip identity based on human feeling of slip. Our slip classification framework performs high accuracy of 97.62% on the test dataset. It is expected to be capable of enhancing the stability of robot grasping significantly, leading to better contact force control, finer object interaction and more active sensing manipulation.
7 pages, 7 figures, submitted to ICRA2019
References in corpus (5)
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Cited by in corpus (8)
- When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors from the Signal Processing Perspective
- Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
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- Vision and Tactile Robotic System to Grasp Litter in Outdoor Environments
- Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor
- Effective Estimation of Contact Force and Torque for Vision-based Tactile Sensor with Helmholtz-Hodge Decomposition
- A Tactile Sensing Foot for Single Robot Leg Stabilization
- Tactile-Based Insertion for Dense Box-Packing