Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning
Author:
Affiliation:
1. School of Mechanical Engineering, Shanghai Jiao Tong University,Shanghai,P.R. China,200240
2. Beijing Institute of Control Engineering,Beijing,P.R. China,100191
Funder
China's National Key Research and Development Program
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10341341/10341342/10342411.pdf?arnumber=10342411
Reference36 articles.
1. Robot learning towards smart robotic manufacturing: A review
2. The feeling of success: Does touch sensing help predict grasp outcomes?;Calandra,2017
3. More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
4. Slip Detection with Combined Tactile and Visual Information
5. Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception
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1. Predicting Maximum Permitted Process Forces for Object Grasping and Manipulation Using a Deep Learning Regression Model;2024 IEEE Conference on Control Technology and Applications (CCTA);2024-08-21
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