@inproceedings{Korkinof:2013,
author = {Korkinof, D and Demiris, Y},
pages = {3222--3229},
publisher = {IEEE},
title = {Online Quantum Mixture Regression for Trajectory Learning by Demonstration},
url = {http://hdl.handle.net/10044/1/12582},
year = {2013}
}

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TY  - CPAPER
AB  - In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quan- tum 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
EP  - 3229
PB  - IEEE
PY  - 2013///
SP  - 3222
TI  - Online Quantum Mixture Regression for Trajectory Learning by Demonstration
UR  - http://hdl.handle.net/10044/1/12582
ER  - 

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