NDLI: Adaptive and Personalised Robots - Learning from Users' Feedback

Content Provider	IEEE Xplore Digital Library
Author	Karami, A.B. Sehaba, K. Encelle, B.
Copyright Year	2013
Description	Author affiliation: LIRIS, Univ. de Lyon, Lyon, France (Karami, A.B.; Sehaba, K.; Encelle, B.)
Abstract	Service robots have become increasingly important subjects in our lives. However, they are still facing problems like adaptability to their users. While major work has focused on intelligent service robots, the proposed approaches were mostly user independent. Our work is part of the FUI-RoboPopuli project, which concentrates on endowing entertainment companion robots with adaptive and social behaviour. In particular, we are interested in robots that are able to learn and plan so that they adapt and personalize their behaviour according to their users. Markov Decision Processes (MDPs) are largely used for adaptive robots applications. However, one challenging point is reducing the sample complexity required to learn an MDP model, including the reward function. In this article, we present our contribution regarding the representation and the learning of the reward function through analysing interaction traces (i.e. the interaction history between the robot and their users, including users' feedback). Our approach permits to generalise the learned rewards so that when new users are introduced, the robot may quickly adapt using what it learned from previous experiences with other users. We propose, in this article, two algorithms to learn the reward function. The first is direct and certain, the robot applies with a user what it learned during interaction with same kind of users (i.e. users with similar profiles). The second algorithm generalises what it learns to be applied to all kinds of users. Through simulation, we show that the generalised algorithm converges to an optimal reward function with less than half the samples needed by the direct algorithm.
Sponsorship	IEEE Comput. Soc.
Starting Page	626
Ending Page	632
File Size	322413
Page Count	7
File Format	PDF
ISSN	10823409
e-ISBN	9781479929726
DOI	10.1109/ICTAI.2013.98
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Hidden Markov models History Service robots Brightness Markov processes Markov Decision Processes MDPs Adaptive and personalised robots Learning from users feedback
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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