Generalized Visual-Tactile Transformer Network for Slip Detection
نویسندگان
چکیده
منابع مشابه
Slip Detection with Combined Tactile and Visual Information
Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forc...
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We present a deep convolutional neural network which is capable to distinguish between different contact states in robotic manipulation tasks. By integrating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotation...
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Dexterous grasping and manipulation of objects with robot hands requires the ability to monitor contact locations in real-time and with good spatial resolution in order to close the control loop required for object and contact trajectory generation. The ability to recognize incipient slippage will allow for autonomous grasp force adaption – a major prerequisite to handle objects of unknown weig...
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Tactile sensing provides critical information, such as force, texture, shape or temperature, in manipulation tasks. In particular, tactile sensors traditionally used in robotics are emphasized in contact force determination for grasping control and object recognition. Nevertheless, slip detection is also crucial to successfully manipulate an object. Several approaches have appeared to detect sl...
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2020
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2020.12.2430