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Reinforcement learning of bimanual r...

Colome, Adria.

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Reinforcement learning of bimanual robot skills

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reinforcement learning of bimanual robot skills/ by Adria Colome, Carme Torras.
Author:	Colome, Adria.
other author:	Torras, Carme.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xix, 182 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- State of the art -- Inverse kinematics and relative arm positioning -- Robot compliant control -- Preliminaries -- Sampling efficiency in learning robot motion -- Dimensionality reduction with MPs -- Generating and adapting ProMPs -- Conclusions.
Contained By:	Springer Nature eBook
Subject:	Robots - Kinematics. -
Online resource:	https://doi.org/10.1007/978-3-030-26326-3
ISBN:	9783030263263

Reinforcement learning of bimanual robot skills
Colome, Adria.

Reinforcement learning of bimanual robot skills[electronic resource] /by Adria Colome, Carme Torras. - Cham :Springer International Publishing :2020. - xix, 182 p. :ill. (some col.), digital ;24 cm. - Springer tracts in advanced robotics,v.1341610-7438 ;. - Springer tracts in advanced robotics ;v.134..

Introduction -- State of the art -- Inverse kinematics and relative arm positioning -- Robot compliant control -- Preliminaries -- Sampling efficiency in learning robot motion -- Dimensionality reduction with MPs -- Generating and adapting ProMPs -- Conclusions.

This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.

ISBN: 9783030263263

Standard No.: 10.1007/978-3-030-26326-3doiSubjects--Topical Terms:

791536
Robots
--Kinematics.

LC Class. No.: TJ211.412 / .C65 2020

Dewey Class. No.: 629.892

Reinforcement learning of bimanual robot skills
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245 1 0 $a Reinforcement learning of bimanual robot skills $h [electronic resource] / $c by Adria Colome, Carme Torras.
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300 $a xix, 182 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer tracts in advanced robotics, $x 1610-7438 ; $v v.134
505 0 $a Introduction -- State of the art -- Inverse kinematics and relative arm positioning -- Robot compliant control -- Preliminaries -- Sampling efficiency in learning robot motion -- Dimensionality reduction with MPs -- Generating and adapting ProMPs -- Conclusions.
520 $a This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning.
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650 2 4 $a Control and Systems Theory. $3 3381515
700 1 $a Torras, Carme. $3 3530242
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