Teaching grasping to a humanoid hand as a generalization of human grasping data

Michele Folgheraiter, Ilario Baragiola, Giuseppina Gini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Humanoid robotics requires new programming tools. Programming by demonstration is good for simple movements, but so far the adaptation for fine movements in grasping is too difficult for it. Grasping of natural objects with a natural hand is known as one of the most difficult problems in robotics. Mathematical models have been developed only for simple hands or for simple objects. In our research we try to use data directly obtained from a human teacher as in imitation learning. To get data from users we built a data glove, we collected data from different experiments, and generalized them through neural networks. Here we discuss the nature of the data collected and their analysis.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsJ.A. Lopez, E. Benfenati, W. Dubitzky
Pages139-150
Number of pages12
Volume3303
Publication statusPublished - 2004
Externally publishedYes
EventInternational Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics - Milan, Italy
Duration: Nov 25 2004Nov 26 2004

Other

OtherInternational Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics
CountryItaly
CityMilan
Period11/25/0411/26/04

Fingerprint

Grasping
Teaching
Robotics
Demonstrations
Mathematical models
Neural networks
Programming
Imitation
Experiments
Generalization
Human
Neural Networks
Mathematical Model
Experiment

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Folgheraiter, M., Baragiola, I., & Gini, G. (2004). Teaching grasping to a humanoid hand as a generalization of human grasping data. In J. A. Lopez, E. Benfenati, & W. Dubitzky (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3303, pp. 139-150)

Teaching grasping to a humanoid hand as a generalization of human grasping data. / Folgheraiter, Michele; Baragiola, Ilario; Gini, Giuseppina.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / J.A. Lopez; E. Benfenati; W. Dubitzky. Vol. 3303 2004. p. 139-150.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Folgheraiter, M, Baragiola, I & Gini, G 2004, Teaching grasping to a humanoid hand as a generalization of human grasping data. in JA Lopez, E Benfenati & W Dubitzky (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 3303, pp. 139-150, International Symposium KELSI 2004: Knowledge Exploration in Life Science Informatics, Milan, Italy, 11/25/04.
Folgheraiter M, Baragiola I, Gini G. Teaching grasping to a humanoid hand as a generalization of human grasping data. In Lopez JA, Benfenati E, Dubitzky W, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 3303. 2004. p. 139-150
Folgheraiter, Michele ; Baragiola, Ilario ; Gini, Giuseppina. / Teaching grasping to a humanoid hand as a generalization of human grasping data. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / J.A. Lopez ; E. Benfenati ; W. Dubitzky. Vol. 3303 2004. pp. 139-150
@inproceedings{e8204305365b42a5808016fc0e37ac16,
title = "Teaching grasping to a humanoid hand as a generalization of human grasping data",
abstract = "Humanoid robotics requires new programming tools. Programming by demonstration is good for simple movements, but so far the adaptation for fine movements in grasping is too difficult for it. Grasping of natural objects with a natural hand is known as one of the most difficult problems in robotics. Mathematical models have been developed only for simple hands or for simple objects. In our research we try to use data directly obtained from a human teacher as in imitation learning. To get data from users we built a data glove, we collected data from different experiments, and generalized them through neural networks. Here we discuss the nature of the data collected and their analysis.",
author = "Michele Folgheraiter and Ilario Baragiola and Giuseppina Gini",
year = "2004",
language = "English",
volume = "3303",
pages = "139--150",
editor = "J.A. Lopez and E. Benfenati and W. Dubitzky",
booktitle = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

}

TY - GEN

T1 - Teaching grasping to a humanoid hand as a generalization of human grasping data

AU - Folgheraiter, Michele

AU - Baragiola, Ilario

AU - Gini, Giuseppina

PY - 2004

Y1 - 2004

N2 - Humanoid robotics requires new programming tools. Programming by demonstration is good for simple movements, but so far the adaptation for fine movements in grasping is too difficult for it. Grasping of natural objects with a natural hand is known as one of the most difficult problems in robotics. Mathematical models have been developed only for simple hands or for simple objects. In our research we try to use data directly obtained from a human teacher as in imitation learning. To get data from users we built a data glove, we collected data from different experiments, and generalized them through neural networks. Here we discuss the nature of the data collected and their analysis.

AB - Humanoid robotics requires new programming tools. Programming by demonstration is good for simple movements, but so far the adaptation for fine movements in grasping is too difficult for it. Grasping of natural objects with a natural hand is known as one of the most difficult problems in robotics. Mathematical models have been developed only for simple hands or for simple objects. In our research we try to use data directly obtained from a human teacher as in imitation learning. To get data from users we built a data glove, we collected data from different experiments, and generalized them through neural networks. Here we discuss the nature of the data collected and their analysis.

UR - http://www.scopus.com/inward/record.url?scp=22944490851&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=22944490851&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:22944490851

VL - 3303

SP - 139

EP - 150

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

A2 - Lopez, J.A.

A2 - Benfenati, E.

A2 - Dubitzky, W.

ER -