Human-in-the-loop control and task learning for pneumatically actuated muscle based robots

Name: Human-in-the-loop control and task learning for pneumatically actuated muscle based robots
Author: Teramae, T., Ishihara, K., Babič, J., Morimoto, J., Öztop, Erhan

İsim	Human-in-the-loop control and task learning for pneumatically actuated muscle based robots
Yazar	Teramae, T., Ishihara, K., Babič, J., Morimoto, J., Öztop, Erhan
Basım Tarihi:	2018-11-06
Basım Yeri	- Frontiers Media
Konu	Human in the loop control, Pneumatically actuated muscle, Biologically inspired multimodal control, Human motor learning, Electromyography
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1662-5218
Kayıt Numarası	1652b3ae-1414-4436-9b09-19737412939d
Lokasyon	Computer Science
Tarih	2018-11-06
Notlar	ImPACT of CSTI ; Commissioned Research of NICT, AMED ; Research and Development of Advanced Medical Devices and Systems to Achieve the future of Medicine from AMED ; JSPS KAKENHI ; NEDO ; Japan Trust ; EC FP7 Converge project ; EU ; JSPS ; Tateishi Science and Technology Foundation
Örnek Metin	Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-To-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-The-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-The-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-The-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.
Editör	Conradt, J.
DOI	10.3389/fnbot.2018.00071
Cilt	12

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Human-in-the-loop control and task learning for pneumatically actuated muscle based robots

Yazar Teramae, T., Ishihara, K., Babič, J., Morimoto, J., Öztop, Erhan

Basım Tarihi 2018-11-06

Basım Yeri - Frontiers Media

Konu Human in the loop control, Pneumatically actuated muscle, Biologically inspired multimodal control, Human motor learning, Electromyography

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1662-5218

Kayıt Numarası 1652b3ae-1414-4436-9b09-19737412939d

Lokasyon Computer Science

Tarih 2018-11-06

Notlar ImPACT of CSTI ; Commissioned Research of NICT, AMED ; Research and Development of Advanced Medical Devices and Systems to Achieve the future of Medicine from AMED ; JSPS KAKENHI ; NEDO ; Japan Trust ; EC FP7 Converge project ; EU ; JSPS ; Tateishi Science and Technology Foundation

Örnek Metin Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-To-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-The-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-The-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-The-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.

Editör Conradt, J.

DOI 10.3389/fnbot.2018.00071

Cilt 12