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Learning objective functions for aut...

Kalakrishnan, Mrinal.

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Learning objective functions for autonomous motion generation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning objective functions for autonomous motion generation./
Author:	Kalakrishnan, Mrinal.
Description:	97 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Contained By:	Dissertation Abstracts International75-11B(E).
Subject:	Engineering, Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3628197
ISBN:	9781321037654

Learning objective functions for autonomous motion generation.
Kalakrishnan, Mrinal.

Learning objective functions for autonomous motion generation. - 97 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--University of Southern California, 2014.

This item must not be sold to any third party vendors.

Planning and optimization methods have been widely applied to the problem of trajectory generation for autonomous robotics. The performance of such methods, however, is critically dependent on the choice of objective function being optimized, and is non-trivial to design. On the other end of the spectrum, efforts on learning autonomous behavior from user-provided demonstrations have largely been focused on reproducing behavior similar in appearance to the provided demonstrations. An alternative approach, known as Inverse Reinforcement Learning (IRL), is to learn an objective function that the demonstrations are assumed to be optimal under. With the help of a planner or trajectory optimizer, such an approach allows the system to synthesize novel behavior in situations that were not experienced in the demonstrations.

ISBN: 9781321037654Subjects--Topical Terms:

1018454
Engineering, Robotics.

Learning objective functions for autonomous motion generation.
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100 1 $a Kalakrishnan, Mrinal. $3 3170026
245 1 0 $a Learning objective functions for autonomous motion generation.
300 $a 97 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
500 $a Adviser: Stefan Schaal.
502 $a Thesis (Ph.D.)--University of Southern California, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Planning and optimization methods have been widely applied to the problem of trajectory generation for autonomous robotics. The performance of such methods, however, is critically dependent on the choice of objective function being optimized, and is non-trivial to design. On the other end of the spectrum, efforts on learning autonomous behavior from user-provided demonstrations have largely been focused on reproducing behavior similar in appearance to the provided demonstrations. An alternative approach, known as Inverse Reinforcement Learning (IRL), is to learn an objective function that the demonstrations are assumed to be optimal under. With the help of a planner or trajectory optimizer, such an approach allows the system to synthesize novel behavior in situations that were not experienced in the demonstrations.
520 $a We present novel algorithms for IRL that have successfully been applied in two real-world, competitive robotics settings: (1) In the domain of rough terrain quadruped locomotion, we present an algorithm that learns an objective function for foothold selection based on "terrain templates". The learner automatically generates and selects the appropriate features which form the objective function, which reduces the need for feature engineering while attaining a high level of generalization. (2) For the domain of autonomous manipulation, we present a probabilistic model of optimal trajectories, which results in new algorithms for inverse reinforcement learning and trajectory optimization in high-dimensional settings. We apply this method to two problems in robotic manipulation: redundancy resolution in inverse kinematics, and trajectory optimization for grasping and manipulation. Both methods have proven themselves as part of larger integrated systems in competitive settings against other teams, where testing was conducted by an independent test team in situations that were not seen during training.
590 $a School code: 0208.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Computer Science. $3 626642
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710 2 $a University of Southern California. $b Computer Science. $3 1023331
773 0 $t Dissertation Abstracts International $g 75-11B(E).
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792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3628197