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Robot learning from demonstration using predictive sequence learning
Department of Computing Science, Umeå University, Sweden.ORCID-id: 0000-0002-6568-9342
Department of Computing Science, Umeå University, Sweden.
Department of Computing Science, Umeå University, Sweden.
2012 (engelsk)Inngår i: Robotic systems: applications, control and programming / [ed] Ashish Dutta, Kanpur, India: IN-TECH , 2012, s. 235-250Kapittel i bok, del av antologi (Fagfellevurdert)
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Abstract [en]

In this chapter, the prediction algorithm Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL generates hypotheses from a sequence of sensory-motor events. Generated hypotheses can be used as a semi-reactive controller for robots. PSL has previously been used as a method for LFD, but suffered from combinatorial explosion when applied to data with many dimensions, such as high dimensional sensor and motor data. A new version of PSL, referred to as Fuzzy Predictive Sequence Learning (FPSL), is presented and evaluated in this chapter. FPSL is implemented as a Fuzzy Logic rule base and works on a continuous state space, in contrast to the discrete state space used in the original design of PSL. The evaluation of FPSL shows a significant performance improvement in comparison to the discrete version of the algorithm. Applied to an LFD task in a simulated apartment environment, the robot is able to learn to navigate to a specific location, starting from an unknown position in the apartment.

sted, utgiver, år, opplag, sider
Kanpur, India: IN-TECH , 2012. s. 235-250
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data- och systemvetenskap
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URN: urn:nbn:se:his:diva-12146DOI: 10.5772/26165ISBN: 978-953-307-941-7 (tryckt)OAI: oai:DiVA.org:his-12146DiVA, id: diva2:1076494
Tilgjengelig fra: 2012-01-02 Laget: 2017-02-22 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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