Zhou Zhao
Tactile-based grasping stability prediction based on human grasp demonstration for robot manipulation
Zhao, Zhou; He, Wenhao; Lu, Zhenyu
Authors
Wenhao He
Zhenyu Lu
Abstract
To minimize irrelevant and redundant information in tactile data and harness the dexterity of human hands. In this paper, we introduce a novel binary classification network with normalized differential convolution (NDConv) layers. Our method leverages the recent progress in visual-based tactile sensing to significantly improve the accuracy of grasp stability prediction. First, we collect a dataset from human demonstration by grasping 15 different daily objects. Then, we rethink pixel correlation and design a novel NDConv layer to fully utilize spatio-temporal information. Finally, the classification network not only achieves a real-time temporal sequence prediction but also obtains an average classification accuracy of 92.97%. The experimental results show that the network can hold a high classification accuracy even when facing unseen objects.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 23, 2024 |
Online Publication Date | Jan 29, 2024 |
Publication Date | Mar 31, 2024 |
Deposit Date | Jun 12, 2024 |
Publicly Available Date | Jun 12, 2024 |
Journal | IEEE Robotics and Automation Letters |
Print ISSN | 2377-3766 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 9 |
Issue | 3 |
Pages | 2646 - 2653 |
DOI | https://doi.org/10.1109/lra.2024.3359553 |
Keywords | Artificial Intelligence, Control and Optimization, Computer Science Applications, Computer Vision and Pattern Recognition, Mechanical Engineering, Human-Computer Interaction, Biomedical Engineering, Control and Systems Engineering |
Public URL | https://uwe-repository.worktribe.com/output/11680684 |
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