Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Korkinof:2013:10.1109/IROS.2013.6696814,
author = {Korkinof, D and Demiris, Y},
doi = {10.1109/IROS.2013.6696814},
pages = {3222--3229},
publisher = {IEEE},
title = {Online Quantum Mixture Regression for Trajectory Learning by Demonstration},
url = {http://dx.doi.org/10.1109/IROS.2013.6696814},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.
AU - Korkinof,D
AU - Demiris,Y
DO - 10.1109/IROS.2013.6696814
EP - 3229
PB - IEEE
PY - 2013///
SN - 2153-0858
SP - 3222
TI - Online Quantum Mixture Regression for Trajectory Learning by Demonstration
UR - http://dx.doi.org/10.1109/IROS.2013.6696814
UR - http://hdl.handle.net/10044/1/12664
ER -