AUTONOMOUS ONLINE LEARNING OF REACHING BEHAVIOR IN A HUMANOID ROBOT-Reference-Cited by-同舟云学术

AUTONOMOUS ONLINE LEARNING OF REACHING BEHAVIOR IN A HUMANOID ROBOT

Published:2012-09 Issue:03 Volume:09 Page:1250017
ISSN:0219-8436
Container-title:International Journal of Humanoid Robotics
language:en
Short-container-title:Int. J. Human. Robot.

Author:

JAMONE LORENZO¹,NATALE LORENZO¹,NORI FRANCESCO¹,METTA GIORGIO²³,SANDINI GIULIO²³

Affiliation:

1. Department of Robotics Brain and Cognitive Sciences, Italian Institute of Technology, Via Morego 30, Genoa, 16163, Italy

2. Department of Robotics Brain and Cognitive Sciences, Italian Institute of Technology Via Morego 30, Genoa, 16163, Italy

3. DIST, University of Genoa Viale Causa 13, Genoa, 16145, Italy

Abstract

In this paper we describe an autonomous strategy which enables a humanoid robot to learn how to reach for a visually identified object in the 3D space. The robot is a 22-DOF upper-body humanoid with moving eyes, neck, arm and hand. The robot is bootstrapped with limited a-priori knowledge, sufficient to start the interaction with the environment; this interaction allows the robot to learn different sensorimotor mappings, required for reaching. The arm-head forward kinematic model and a visuo-motor inverse model are learned from sensory experience. Learning is performed purely online (without any separation between training and execution) through a goal-directed exploration of the environment. During the learning the robot is also able to build an internal representation of its reachable space.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Mechanical Engineering

Link

https://www.worldscientific.com/doi/pdf/10.1142/S021984361250017X

Reference34 articles.

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