A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression

Name: A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression
Author: Peternel, L., Öztop, Erhan, Babič, J.

İsim	A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression
Yazar	Peternel, L., Öztop, Erhan, Babič, J.
Basım Tarihi:	2016
Basım Yeri	- IEEE
Konu	Robot sensing systems, Education, Training data, Predictive models, Force, Robot control
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2153-0866
Kayıt Numarası	35132b84-3016-4f25-9860-ab74aaa331b8
Lokasyon	Computer Science
Tarih	2016
Notlar	European Commission
Örnek Metin	We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.
DOI	10.1109/IROS.2016.7759574

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A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression

Yazar Peternel, L., Öztop, Erhan, Babič, J.

Basım Tarihi 2016

Basım Yeri - IEEE

Konu Robot sensing systems, Education, Training data, Predictive models, Force, Robot control

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2153-0866

Kayıt Numarası 35132b84-3016-4f25-9860-ab74aaa331b8

Lokasyon Computer Science

Tarih 2016

Notlar European Commission

Örnek Metin We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.

DOI 10.1109/IROS.2016.7759574