Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedAT10 Lince Wednesday, October 10, 2012, 08:30–09:30
Skill Learning – Dynamics
Chair Rüdiger Dillmann, KIT Karlsruhe Inst. for Tech.
Co-Chair
08:30–08:45 WedAT10.1
Autonomous Online Learning of Velocity
Kinematics on the iCub: a Comparative Study
Alain Droniou, Serena Ivaldi, Vincent Padois and Olivier Sigaud
Institut des Systèmes Intelligents et de Robotique - CNRS UMR 7222,
Université Pierre et Marie Curie
• Incremental and autonomous online
learning of velocity kinematics, from
scratch and in a limited time
• Visual servoing task with general target
and end-effector (unknown tool)
• Three contexts: reaching the target in one
or two different workspaces; tracking a
target moving unpredictably by a human
• Comparison of three ML algorithms:
LWPR, XCSF and ISSGPR
• Testing generalization capabilities, velocity
in learning and robustness of parameters
• ISSGPR performs better in all studied
criteria
The learning contexts
09:00–09:15 WedAT10.3
Learning Concurrent Motor Skills in Versatile
Solution Spaces
Christian Daniel, Gerhard Neumann and Jan Peters
FG Intelligent Autonomous Systems, TU Darmstadt, Germany
Max Planck Institute for Intelligent Systems, Germany
• Many interesting motor skill tasks
have several distinct solutions.
• Representing multiple solutions
ensures operability of the robot
even if the environment changes
and can in addition lead to faster
learning.
• We present a hierarchical policy
search method which can
simultaneously learn multiple
motor skills to solve complex
tasks.
08:45–09:00 WedAT10.2
Online Learning of Inverse Dynamics via
Gaussian Process Regression
Joseph Sun de la Cruz
National Instruments, USA
Bill Owen Dana Kulić
University of Waterloo, Canada
• On-line learning of the inverse dynamics of a robot manipulator with
Gaussian Process Regression
• Model is trained on a sparse subset of the observed data, with incremental
updates to both the model and the hyper-parameters
• Investigate the impact of full or partial prior information on the convergence
• Comparison to existing approaches shows improved accuracy and reduced
computational requirements
Computation Time Required for a Single Prediction
09:15–09:30 WedAT10.4
Learning Robot Dynamics with
Kinematic Bézier Maps
Stefan Ulbrich, Michael Bechtel,
Tamim Asfour and Rüdiger Dillmann
Humanoids and Intelligence Systems Lab
Institute for Anthropomatics at the Karlsruhe Institute for Technology
• Novel model-based machine
learning algorithm
• Tailored to efficiently learn the
inverse dynamics
• Based on the Kinematic Bézier
Maps algorithm
• Exact encoding of the equations of
motion
• Batch and incremental (online)
learning
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–128–
Plot of the Coriolis and centripetal
forces on a robot joint