Online multimodal ensemble learning using self-learned sensorimotor representations

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Title: Online multimodal ensemble learning using self-learned sensorimotor representations
Author(s): Zambelli, M
Demiris, Y
Item Type: Journal Article
Abstract: Internal models play a key role in cognitive agents by providing on the one hand predictions of sensory consequences of motor commands (forward models), and on the other hand inverse mappings (inverse models) to realise tasks involving control loops, such as imitation tasks. The ability to predict and generate new actions in continuously evolving environments intrinsically requiring the use of different sensory modalities is particularly relevant for autonomous robots, which must also be able to adapt their models online. We present a learning architecture based on self-learned multimodal sensorimotor rep- resentations. To attain accurate forward models, we propose an online heterogeneous ensemble learning method that allows us to improve the prediction accuracy by leveraging differences of multiple diverse predictors. We further propose a method to learn inverse models on-the-fly to equip a robot with multimodal learning skills to perform imitation tasks using multiple sensory modalities. We have evaluated the proposed methods on an iCub humanoid robot. Since no assumptions are made on the robot kinematic/dynamic structure, the method can be applied to different robotic platforms.
Publication Date: 2-Nov-2016
Date of Acceptance: 11-Oct-2016
ISSN: 2379-8920
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 113
End Page: 126
Journal / Book Title: IEEE Transactions on Cognitive and Developmental Systems
Volume: 9
Issue: 2
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 612139
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science
Neurosciences & Neurology
Ensemble learning
multimodal imitation learning
online learning
sensorimotor contingencies
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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