Imperial College London
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ProfessorEricYeatman

Faculty of Engineering, Department of Electrical and Electronic Engineering

Head of Department of Electrical and Electronic Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6204e.yeatman CV

 
 
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Assistant

 

Ms Anna McCormick +44 (0)20 7594 6189

 
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Location

 

610aElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cursi:2022:10.1109/LRA.2022.3180428,
author = {Cursi, F and Bai, W and Li, W and Yeatman, EM and Kormushev, P},
doi = {10.1109/LRA.2022.3180428},
journal = {IEEE Robotics and Automation Letters},
pages = {7140--7147},
title = {Augmented neural network for full robot kinematic modelling in SE(3)},
url = {http://dx.doi.org/10.1109/LRA.2022.3180428},
volume = {7},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, the inverse kinematics is directly learned, yet, learning the forward kinematics is simpler and allows computing exploiting the optimality of the controllers. Nevertheless, the learning method has no knowledge about the differential relationship between the position and velocity mappings. Currently, few works have targeted learning full robot poses considering both position and orientation. In this letter, we present a novel feedforward Artificial Neural network (ANN) architecture to learn full robot pose in SE(3) incorporating differential relationships in the learning process. Simulation and real world experiments show the capabilities of the proposed network to properly model the robot pose and its advantages over standard ANN.
AU  - Cursi,F
AU  - Bai,W
AU  - Li,W
AU  - Yeatman,EM
AU  - Kormushev,P
DO  - 10.1109/LRA.2022.3180428
EP  - 7147
PY  - 2022///
SN  - 2377-3766
SP  - 7140
TI  - Augmented neural network for full robot kinematic modelling in SE(3)
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/LRA.2022.3180428
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000811580800018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - http://hdl.handle.net/10044/1/102811
VL  - 7
ER  -
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