Predictive learning from demonstration
2011 (English)In: Agents and artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers / [ed] Filipe, Joaquim, Fred, Ana, Sharp, Bernadette, Berlin: Springer Verlag , 2011, 1, 186-200 p.Chapter in book (Refereed)
A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.
Place, publisher, year, edition, pages
Berlin: Springer Verlag , 2011, 1. 186-200 p.
Communications in Computer and Information Science, ISSN 1865-0929 ; 129
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject Computer and Information Science
IdentifiersURN: urn:nbn:se:umu:diva-44191DOI: 10.1007/978-3-642-19890-8_14ISI: 000302748200014ISBN: 978-3-642-19889-2 (print)OAI: oai:DiVA.org:umu-44191DiVA: diva2:419040
Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010