A Modular Dynamic Sensorimotor Model for Affordances Learning, Sequences Planning, and Tool-Use

This paper proposes a computational model for learning robot control and sequence planning based on the ideomotor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits these primitive skills to build complex action sequences, potentially involvin...

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Veröffentlicht in:	IEEE transactions on cognitive and developmental systems Jg. 10; H. 1; S. 72 - 87
Hauptverfasser:	Braud, Raphael, Pitti, Alexandre, Gaussier, Philippe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers, Inc
Schlagworte:	Affordance Artificial Intelligence Cognitive science Computational modeling Computer Science Computer Vision and Pattern Recognition Embedded Systems Encoding Human-Computer Interaction ideomotor principle Image Processing Learning Machine Learning Modeling and Simulation Modular systems Neural and Evolutionary Computing Neuroscience Object recognition Planning Psychology Robot arms Robot control Robot kinematics Robot sensing systems Robotics sensorimotor laws sequences tool-use Sequences Ideomotor principle Tool-Use Affordance Sensorimotor Laws Robotics
ISSN:	2379-8920, 2379-8939
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Abstract	This paper proposes a computational model for learning robot control and sequence planning based on the ideomotor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits these primitive skills to build complex action sequences, potentially involving tool-use. Implemented for a robotic arm, the model starts with raw unlabeled sensor and motor vectors and autonomously assigns functions to neutral objects in the environment. Our experimental evaluation highlights the emergent properties of such a modular system and we discuss their consequences from ideomotor and sensorimotor-theoretic perspectives.
AbstractList	This paper proposes a computational model for learning robot control and sequence planning based on the ideomotor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits these primitive skills to build complex action sequences, potentially involving tool-use. Implemented for a robotic arm, the model starts with raw unlabeled sensor and motor vectors and autonomously assigns functions to neutral objects in the environment. Our experimental evaluation highlights the emergent properties of such a modular system and we discuss their consequences from ideomotor and sensorimotor-theoretic perspectives. This paper proposes a computational model for learning robot control and sequence planning based on the ideo-motor principle. This model encodes covariation laws between sensors and motors in a modular fashion and exploits these primitive skills to build complex action sequences, potentially involving tool-use. Implemented for a robotic arm, the model starts with raw unlabelled sensor and motor vectors and autonomously assigns functions to neutral objects in the environment. Our experimental evaluation highlights the emergent properties of such a modular system and we discuss their consequences from ideomotor and sensorimotor-theoretic perspectives.
Author	Braud, Raphael Pitti, Alexandre Gaussier, Philippe
Author_xml	– sequence: 1 givenname: Raphael surname: Braud fullname: Braud, Raphael email: raphael.braud@gmail.com organization: Lab. ETIS, Univ. of Cergy-Pontoise, Pontoise, France – sequence: 2 givenname: Alexandre surname: Pitti fullname: Pitti, Alexandre organization: Lab. ETIS, Univ. of Cergy-Pontoise, Pontoise, France – sequence: 3 givenname: Philippe surname: Gaussier fullname: Gaussier, Philippe organization: Lab. ETIS, Univ. of Cergy-Pontoise, Pontoise, France
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SubjectTerms	Affordance Artificial Intelligence Cognitive science Computational modeling Computer Science Computer Vision and Pattern Recognition Embedded Systems Encoding Human-Computer Interaction ideomotor principle Image Processing Learning Machine Learning Modeling and Simulation Modular systems Neural and Evolutionary Computing Neuroscience Object recognition Planning Psychology Robot arms Robot control Robot kinematics Robot sensing systems Robotics sensorimotor laws sequences tool-use
Title	A Modular Dynamic Sensorimotor Model for Affordances Learning, Sequences Planning, and Tool-Use
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