Active Estimation of Object Dynamics Parameters with Tactile Sensors
Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2010), Taiwan (2010).
Date
2010Author
Saal, Hannes
Ting, Jo-Anne
Vijayakumar, Sethu
Metadata
Abstract
The estimation of parameters that affect the
dynamics of objects—such as viscosity or internal degrees of
freedom—is an important step in autonomous and dexterous
robotic manipulation of objects. However, accurate and efficient
estimation of these object parameters may be challenging due
to complex, highly nonlinear underlying physical processes. To
improve on the quality of otherwise hand-crafted solutions,
automatic generation of control strategies can be helpful.
We present a framework that uses active learning to help
with sequential gathering of data samples, using informationtheoretic
criteria to find the optimal actions to perform at each
time step. We demonstrate the usefulness of our approach on a
robotic hand-arm setup, where the task involves shaking bottles
of different liquids in order to determine the liquid’s viscosity
from only tactile feedback. We optimize the shaking frequency
and the rotation angle of shaking in an online manner in order
to speed up convergence of estimates.