Multimodal learning of demonstrated grasping skills for flexibly handling grasped objects
Author(s):
M. Hüser, T. Baier, D. Westhoff and J. Zhang
in:
ISR/Robotik 2006, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik, Mai 2006
Abstract
In this paper a novel approach to learning by
demonstration (LbD) is presented. A multimodal service robot is
taught grasping skills by a human instructor who demonstrates
a grasping action. Our approach contributes novel solutions to
the aspects of robustly tracking the demonstrator's hands in real
time as well as to the transformation of tracking results into
grasping skills. To track the demonstrator's hands in stereoscopic
images a Mean-Shift-like algorithm is adapted. For the very first
time this algorithm is applied to local binary patterns (LBP)
and color histograms. To retrieve the hand configuration we
use view-based Principal Component Analysis (PCA). To develop
grasping skills from tracking results the robot repetitively tracks
the demonstrator's grasping actions and transforms the results
into three-dimensional self organizing maps (SOMs). The SOMs
give a spatial description of the collected data and serve as data
structures for a reinforcement learning (RL) algorithm which
optimizes trajectories for use by the robot. The approach is
applied to a multimodal service robot. Experiments show the
effectiveness of the LBP-enhanced Mean-Shift-like tracking and
the robustness of LbD based on SOMs and RL
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