Title
Teaching Tasks to Multiple Small Robots by Classifying and Splitting a Human Example.
Abstract
In this study, we present a novel framework to address the problem of teaching manipulation tasks performed by a single human to a set of multiple small robots in a short period. First, we focused on classifying the manipulation style used during a human-performed task. An allocator process is proposed to determine the type and number of robots to be taught based on the capabilities of available robots. Then, according to the detected task requirements, robot behaviors are generated to create robot programs by splitting human demonstration data. Small robots were used to evaluate our approach in four defined tasks that were taught by a single human. Experiments demonstrated the efficiency of the method to classify and judge whether the division of a task is necessary or not. Moreover, robot programs were generated for manipulating selected objects either individually or in a cooperative manner.
Year
DOI
Venue
2017
10.20965/jrm.2017.p0419
JOURNAL OF ROBOTICS AND MECHATRONICS
Keywords
Field
DocType
teaching multiple robots,human-robot interaction,cooperative manipulation
Computer science,Artificial intelligence,Robot,Machine learning,Human–robot interaction
Journal
Volume
Issue
ISSN
29
SP2
0915-3942
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
Jorge David112.73
Jose Ildefonso U. Rubrico2103.84
Shouhei Shirafuji32010.19
Jun Ota4527109.77