NDLI: Surprise-Based Learning for Developmental Robotics

Content Provider	IEEE Xplore Digital Library
Author	Ranasinghe, N. Wei-Min Shen
Copyright Year	2008
Description	Author affiliation: Inf. Sci. Inst., Univ. of Southern California, Los Angeles, CA (Ranasinghe, N.; Wei-Min Shen)
Abstract	This paper presents a learning algorithm called surprise-based learning (SBL) capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of the environment prior to the execution of an action and the forecasted or predicted observation of the environment after the action. The algorithm learns by investigating "surprises", which are inconsistencies between the predictions and observed outcome. SBL has been successfully demonstrated on a modular robot learning and navigating in a small static environment.
Starting Page	65
Ending Page	70
File Size	317087
Page Count	6
File Format	PDF
ISBN	9780769532721
DOI	10.1109/LAB-RS.2008.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-06
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	world model Predictive models complementary discrimination autonomous robot reinforcement learning features developmental robotics navigation surprise-based learning Robot sensing systems Feature extraction Prediction algorithms predict Sensors Planning plan Robots
Content Type	Text
Resource Type	Article

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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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