Learning Motor Skills - Intelligent Autonomous Systems

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Learning Motor Skills: From Algorithms to Robot Experiments Erlernen Motorischer Fähigkeiten: Von Algorithmen zu Roboter-Experimenten Vorgelegte Dissertation von Dipl.-Ing. Jens Kober aus Künzelsau 1. Gutachten: Prof. Dr. Jan Peters 2. Gutachten: Prof. Dr. Stefan Schaal Tag der Einreichung: Darmstadt — D 17 Bitte zitieren Sie dieses Dokument als: URN: urn:nbn:de:tuda-tuprints-12345 URL: http://tuprints.ulb.tu-darmstadt.de/12345 Dieses Dokument wird bereitgestellt von tuprints, E-Publishing-Service der TU Darmstadt http://tuprints.ulb.tu-darmstadt.de tuprints@ulb.tu-darmstadt.de Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung – Keine kommerzielle Nutzung – Keine Bearbeitung 2.0 Deutschland http://creativecommons.org/licenses/by-nc-nd/2.0/de/
Erklärung zur Dissertation Hiermit versichere ich, die vorliegende Dissertation ohne Hilfe Dritter nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus Quellen entnommen wurden, sind als solche kenntlich gemacht. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen. Darmstadt, den 13. März 2012 (J. Kober) i
Page 1: Learning Motor Skills: From Algorit
Page 6 and 7: epresenting sub-tasks, can be combi
Page 8 and 9: durch das Anpassen einer kleinen An
Page 11 and 12: Contents Abstract Zusammenfassung A
Page 13: 6 Learning Prioritized Control of M
Page 16 and 17: Policy search, also known as policy
Page 18 and 19: Figure 1.1: This figure illustrates
Page 21 and 22: 2 Reinforcement Learning in Robotic
Page 23 and 24: (a) OBELIX robot (b) Zebra Zero rob
Page 25 and 26: the task’s performance. This prob
Page 27 and 28: the policy is considered a conditio
Page 29 and 30: POLICY SEARCH Approach Employed by.
Page 31 and 32: SMART STATE-ACTION DISCRETIZATION A
Page 33 and 34: Figure 2.3: Boston Dynamics LittleD
Page 35 and 36: DEMONSTRATION Approach Employed by.
Page 37 and 38: Benefits of Noise: A complex real-w
Page 39 and 40: (a) Schematic drawings of the ball-
Page 41 and 42: The policy converges to the maximum
Page 43: obot reinforcement learning tractab
Page 46 and 47: Figure 3.1: This figure illustrates
Page 48 and 49: This canonical system has the time
Page 50 and 51: 1 0.8 0.6 0.4 0.2 0 0 0.5 1 1.5 3 2
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with f ref containing the values of
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Figure 3.7: Generalization to vario
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We evaluate the algorithms derived
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2007], such a derivation results in
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Algorithm 4.2 episodic Natural Acto
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Algorithm 4.3 episodic Reward Weigh
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Open Parameters DoF Rollouts Policy
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(a) minimum motor command (b) passi
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1 average return 0.9 0.8 0.7 0.6 FD
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Figure 4.7: This figure shows the i
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Figure 4.10: This figure illustrate
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1 0.8 average return 0.6 0.4 0.2 0
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updates in simulation (such as Dyna
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complex. We modeled the system in a
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and, thus, ∂ θ log π = σ −2
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5 Reinforcement Learning to Adjust
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parameters for scaling the duration
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Figure 5.2: This figure illustrates
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egression solution w = (Φ T RΦ +
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100 (a) Velocity 2 (b) Precision 3
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The setup is given as follows: A to
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1.4 1.2 Cost−regularized Kernel R
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(a) Left. (b) Half left. (c) Center
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0.9 cost/success 0.7 0.5 0.3 Succes
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position and velocity of the arm, t
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Figure 5.21: This figure illustrate
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favor behaviors with a higher numbe
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are possible. This redundancy can b
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6.2.1 Single Primitive Control Law
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(a) Exaggerated schematic drawing.
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Dominance Structure Number of Hits
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7 Conclusion In this thesis, we hav
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Learning Motor Skills The presented
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Learning Layers Jointly For the bal
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Book Chapters J. Kober and J. Peter
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J. A. Bagnell and J. C. Schneider.
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H. Fässler, H. A. Beyer, and J. T.
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F. Kirchner. Q-learning of complex
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J. A. Martín H., J. de Lope, and D
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L. Peshkin. Reinforcement Learning
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F. Sehnke, C. Osendorfer, T. Rücks
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A. Ude, A. Gams, T. Asfour, and J.
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3.2 In this figure, we convey the i
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4.11 This figure shows schematic dr
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5.17 This figure illustrates the si
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List of Tables 2.1 This table illus
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As symbols in this PhD thesis, the
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Learning Motor Skills - Intelligent Autonomous Systems

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