rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Learning Motor Skills
    sub title: From Algorithms to Robot Experiments
  name:
    name part: Kober, Jens.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Peters, Jan.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 191 p. 56 illus., 54 illus. in color. online resource.
  abstract: This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
  table of contents: Reinforcement Learning in Robotics: A Survey -- Movement Templates for Learning of Hitting and Batting -- Policy Search for Motor Primitives in Robotics -- Reinforcement Learning to Adjust Parameterized Motor Primitives to New Situations -- Learning Prioritized Control of Motor Primitives.
  note: by Jens Kober, Jan Peters.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Engineering
  subject:
    topic: Robotics and Automation
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TJ210.2-211.495
  classification: T59.5
  classification: 629.892
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Tracts in Advanced Robotics, 97
  identifier: 9783319031941
  identifier: http://dx.doi.org/10.1007/978-3-319-03194-1
  record info:
    record creation date: 131123
    record change date: 20151103123539.0
    record identifier: 978-3-319-03194-1