Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors-Reference-Cited by-同舟云学术

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Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors

Published:2020-11 Issue: Volume: Page:
ISSN:
Container-title:2020 Fourth IEEE International Conference on Robotic Computing (IRC)
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Author:

Begalinova Ainur,King Ross D.,Lennox Barry,Batista-Navarro Riza

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9287817/9287880/09287944.pdf?arnumber=9287944

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Touch and slippage detection in robotic hands with spiking neural networks;Engineering Applications of Artificial Intelligence;2024-10

2. Slippage Classification in Prosthetic Hands with a Spiking Neural Network;IFMBE Proceedings;2024

3. Stability Analysis of 3C Electronic Industry Robot Grasping Based on Visual-Tactile Sensing;2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI);2023-12-14

4. Deep Learning LSTM-Based Slip Detection for Robotic Grasping;IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society;2023-10-16

5. Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network;2022 International Conference on Robotics and Automation (ICRA);2022-05-23

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