A Theoretical Framework for End-to-End Learning of Deep Neural Networks With Applications to Robotics
Author:
Affiliation:
1. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore
Funder
Ministry of Education (MOE) Singapore, Academic Research Fund (AcRF) Tier 1
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10053845.pdf?arnumber=10053845
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1. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model
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5. The limits and potentials of deep learning for robotics
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