English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Combining active learning and reactive control for robot grasping

Kroemer, O., Detry, R., Piater, J., & Peters, J. (2010). Combining active learning and reactive control for robot grasping. Robotics and Autonomous Systems, 58(9), 1105-1116. doi:10.1016/j.robot.2010.06.001.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE48-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6AAA-8
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Kroemer, O1, 2, Author              
Detry, R, Author
Piater, J, Author              
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp requires learning about the object as a whole, the execution only needs to reactively adapt to the context close to the grasp’s location. We propose a hierarchical controller that reflects the structure of these two sub-problems, and attempts to learn solutions that work for both. A hybrid architecture is employed by the controller to make use of various machine learning methods that can cope with the large amount of uncertainty inherent to the task. The controller’s upper level selects where to grasp the object using a reinforcement learner, while the lower level comprises an imitation learner and a vision-based reactive controller to determine appropriate grasping motions. The resulting system is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshapin g the hand depending on the object’s geometry. The system was evaluated both in simulation and on a real robot.

Details

show
hide
Language(s):
 Dates: 2010-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.robot.2010.06.001
BibTex Citekey: 6636
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Robotics and Autonomous Systems
  Other : Robotics and autonomous systems : international journal
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 58 (9) Sequence Number: - Start / End Page: 1105 - 1116 Identifier: ISSN: 0921-8890
CoNE: https://pure.mpg.de/cone/journals/resource/954925565691