NDLI: How to train your robot - teaching service robots to reproduce human social behavior

Content Provider	IEEE Xplore Digital Library
Author	Liu, P. Glas, D.F. Kanda, T. Ishiguro, H. Hagita, N.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Eng. Sci., Osaka Univ., Toyonaka, Japan (Ishiguro, H.) \|\| ATR Intell. Robot. & Commun. Labs., Keihanna, Japan (Liu, P.; Glas, D.F.; Kanda, T.; Hagita, N.)
Abstract	Developing interactive behaviors for social robots presents a number of challenges. It is difficult to interpret the meaning of the details of people's behavior, particularly non-verbal behavior like body positioning, but yet a social robot needs to be contingent to such subtle behaviors. It needs to generate utterances and non-verbal behavior with good timing and coordination. The rules for such behavior are often based on implicit knowledge and thus difficult for a designer to describe or program explicitly. We propose to teach such behaviors to a robot with a learning-by-demonstration approach, using recorded human-human interaction data to identify both the behaviors the robot should perform and the social cues it should respond to. In this study, we present a fully unsupervised approach that uses abstraction and clustering to identify behavior elements and joint interaction states, which are used in a variable-length Markov model predictor to generate socially-appropriate behavior commands for a robot. The proposed technique provides encouraging results despite high amounts of sensor noise, especially in speech recognition. We demonstrate our system with a robot in a shopping scenario.
Starting Page	961
Ending Page	968
File Size	594448
Page Count	8
File Format	PDF
ISBN	9781479967636
e-ISBN	9781479967650
DOI	10.1109/ROMAN.2014.6926377
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-25
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Joints Speech Speech recognition Cameras Trajectory
Content Type	Text
Resource Type	Article

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