Robotic grasping and manipulation through human visuomotor learning

Name: Robotic grasping and manipulation through human visuomotor learning
Author: Moore, B., Öztop, Erhan

İsim	Robotic grasping and manipulation through human visuomotor learning
Yazar	Moore, B., Öztop, Erhan
Basım Tarihi:	2012-03
Basım Yeri	- Elsevier
Konu	Body schema, Skill synthesis, Hand control, Robot grasping, Robot manipulation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0921-8890
Kayıt Numarası	377dc6e0-503a-4226-9b63-7bceffca2ca9
Lokasyon	Computer Science
Tarih	2012-03
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	A major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ballswapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object.
DOI	10.1016/j.robot.2011.09.002
Cilt	60

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Robotic grasping and manipulation through human visuomotor learning

Yazar Moore, B., Öztop, Erhan

Basım Tarihi 2012-03

Basım Yeri - Elsevier

Konu Body schema, Skill synthesis, Hand control, Robot grasping, Robot manipulation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0921-8890

Kayıt Numarası 377dc6e0-503a-4226-9b63-7bceffca2ca9

Lokasyon Computer Science

Tarih 2012-03

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin A major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ballswapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object.

DOI 10.1016/j.robot.2011.09.002

Cilt 60