Title:
Inferring Object Properties from Incidental Contact with a Tactile-Sensing Forearm
Inferring Object Properties from Incidental Contact with a Tactile-Sensing Forearm
Author(s)
Bhattacharjee, Tapomayukh
Rehg, James M.
Kemp, Charles C.
Rehg, James M.
Kemp, Charles C.
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Abstract
Whole-arm tactile sensing enables a robot to sense
properties of contact across its entire arm. By using this large
sensing area, a robot has the potential to acquire useful information
from incidental contact that occurs while performing
a task. Within this paper, we demonstrate that data-driven
methods can be used to infer mechanical properties of objects
from incidental contact with a robot’s forearm. We collected
data from a tactile-sensing forearm as it made contact with
various objects during a simple reaching motion. We then used
hidden Markov models (HMMs) to infer two object properties
(rigid vs. soft and fixed vs. movable) based on low-dimensional
features of time-varying tactile sensor data (maximum force,
contact area, and contact motion). A key issue is the extent
to which data-driven methods can generalize to robot actions
that differ from those used during training. To investigate this
issue, we developed an idealized mechanical model of a robot
with a compliant joint making contact with an object. This
model provides intuition for the classification problem. We also
conducted tests in which we varied the robot arm’s velocity and
joint stiffness. We found that, in contrast to our previous methods
[1], multivariate HMMs achieved high cross-validation accuracy
and successfully generalized what they had learned to new robot
motions with distinct velocities and joint stiffnesses.
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Date Issued
2014-09
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Text
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Pre-print