NDLI: A developmental perspective on humanoid skill learning using a hierarchical SOM-based encoding

Content Provider	IEEE Xplore Digital Library
Author	Pierris, G. Dahl, T.S.
Copyright Year	2014
Description	Author affiliation: Cognitive Robot. Res. Centre, Univ. of Wales, Newport, UK (Pierris, G.) \|\| Centre for Neural & Robotic Syst., Plymouth Univ., Plymouth, UK (Dahl, T.S.)
Abstract	Hand-coding is an impractical approach to developing motion repertoires for humanoid robots, requiring both task and programming expertise. Physical demonstration of skills, on the other hand, is an approach with which humans are both competent and familiar. When following a programming-by-demonstration approach, the adaptiveness of a robot can be further increased by giving it the ability to compose novel skills from skills already acquired from demonstration. We have previously presented [1] an extension to the Piaget-inspired Constructivist Learning Architecture [2], featuring a hierarchical SOM-based algorithm that encodes skills as a hierarchy of fixed-length subsequences. At the core of the extended algorithm lies a novel principle for comparing long-term memory and short-term memory, represented as connection weights and decaying node activation values, respectively. In this article, we present an in-depth analysis of how this comparison, can provide a robot control algorithm that is both state-sensitive and goal oriented. We present results from experiments using an abstract chain walk problem that includes hidden states, to demonstrate how the algorithm disambiguates states and selects actions yielding higher rewards. Furthermore, we present results from an experiment where we use programming-by-demonstration to encode and reproduce a figure-8 gesture with a Nao humanoid robot. The results show that our algorithm is capable of identifying hidden states in both real and abstract problem domains.
Starting Page	708
Ending Page	715
File Size	2472736
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Hidden Markov models Robot kinematics Joints Humanoid robots Abstracts
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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