Effective robot skill synthesis via divided control

Name: Effective robot skill synthesis via divided control
Author: Kaya, Osman, Öztop, Erhan

İsim	Effective robot skill synthesis via divided control
Yazar	Kaya, Osman, Öztop, Erhan
Basım Tarihi:	2018-07-02
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-7281-0377-8
Kayıt Numarası	81d9d2d3-4cfd-45a1-a72d-a12139e037c5
Lokasyon	Computer Science
Tarih	2018-07-02
Notlar	Seventh Framework Programme
Örnek Metin	Learning from demonstration is a powerful method for obtaining task skills, which aim to eliminate the need for explicit robot programming. Classically, the tasks are demonstrated to the robot by means of either recorded human motion, direct kinesthetic teaching or through manual interfaces, which may not be applicable for task that involve dynamics. In such cases, human-in-the-Ioop robot learning with anthropomorphic and intuitive tele-operation may be more suitable. In this paper, we propose a divide-and-conquer approach for human-in-the-Ioop robot learning framework to improve the efficacy of skill synthesis. Usually a straightforward division of control between the human and the robot for skill transfer can be designed for effective skill transfer. With such a division, not only the human learning is sped up, but also the design of the autonomous part of the control policy is simplified by exploiting the human capability to learn to adapt to robot operation. In this study, the proposed approach is realized by using the `ball swapping task' on an anthropomorphic robotic arm-hand setup, where the balls must be swapped over the fingers without being dropped. In the current implementation, the control is divided over the control of the arm and the hand, where the human learns to control the position and the orientation of the hand to swap the balls, while the hand runs a periodic finger movement autonomously. Our results indicate that complex autonomous policies can be easily obtained by distributing control over the human operator and the robot in a human-in-the-loop control setup. In particular, we show that the human operator quickly learns to control the arm in such a way that the simple finger movements of the hand become effective ball swapping actions. The combination of human and robot control then yields an autonomous ball swapping skill, which can be further improved for speed.
DOI	10.1109/ROBIO.2018.8664825

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Effective robot skill synthesis via divided control

Yazar Kaya, Osman, Öztop, Erhan

Basım Tarihi 2018-07-02

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-7281-0377-8

Kayıt Numarası 81d9d2d3-4cfd-45a1-a72d-a12139e037c5

Lokasyon Computer Science

Tarih 2018-07-02

Notlar Seventh Framework Programme

Örnek Metin Learning from demonstration is a powerful method for obtaining task skills, which aim to eliminate the need for explicit robot programming. Classically, the tasks are demonstrated to the robot by means of either recorded human motion, direct kinesthetic teaching or through manual interfaces, which may not be applicable for task that involve dynamics. In such cases, human-in-the-Ioop robot learning with anthropomorphic and intuitive tele-operation may be more suitable. In this paper, we propose a divide-and-conquer approach for human-in-the-Ioop robot learning framework to improve the efficacy of skill synthesis. Usually a straightforward division of control between the human and the robot for skill transfer can be designed for effective skill transfer. With such a division, not only the human learning is sped up, but also the design of the autonomous part of the control policy is simplified by exploiting the human capability to learn to adapt to robot operation. In this study, the proposed approach is realized by using the `ball swapping task' on an anthropomorphic robotic arm-hand setup, where the balls must be swapped over the fingers without being dropped. In the current implementation, the control is divided over the control of the arm and the hand, where the human learns to control the position and the orientation of the hand to swap the balls, while the hand runs a periodic finger movement autonomously. Our results indicate that complex autonomous policies can be easily obtained by distributing control over the human operator and the robot in a human-in-the-loop control setup. In particular, we show that the human operator quickly learns to control the arm in such a way that the simple finger movements of the hand become effective ball swapping actions. The combination of human and robot control then yields an autonomous ball swapping skill, which can be further improved for speed.

DOI 10.1109/ROBIO.2018.8664825