Online multimodal ensemble learning using self-learned sensorimotor representations
File(s)
Accepted version
Author(s)
Zambelli, M
Demiris, Y
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.
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.
Date Issued
2016-11-02
Date Acceptance
2016-10-11
Citation
IEEE Transactions on Cognitive and Developmental Systems, 2016, 9 (2), pp.113-126
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
Commission of the European Communities
Grant Number
612139
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Robotics
Neurosciences
Computer Science
Neurosciences & Neurology
Ensemble learning
multimodal imitation learning
online learning
sensorimotor contingencies
MOTOR CONTROL
ONE-SHOT
GAUSSIAN-PROCESSES
HUMANOID ROBOTS
IMITATION
MODELS
REGRESSION
FRAMEWORK
SYSTEMS
SKILLS
Publication Status
Published