Title:
Learning Object Models for Humanoid Manipulation
Learning Object Models for Humanoid Manipulation
Author(s)
Stilman, Mike
Nishiwaki, Koichi
Kagami, Satoshi
Nishiwaki, Koichi
Kagami, Satoshi
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Abstract
We present a successful implementation of rigid
grasp manipulation for large objects moved along specified
trajectories by a humanoid robot. HRP-2 manipulates tables on
casters with a range of loads up to its own mass. The robot
maintains dynamic balance by controlling its center of gravity
to compensate for reflected forces. To achieve high performance
for large objects with unspecified dynamics the robot learns a
friction model for each object and applies it to torso trajectory
generation. We empirically compare this method to a purely
reactive strategy and show a significant increase in predictive
power and stability.
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Date Issued
2007-11
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Text
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Proceedings