56 research outputs found
A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
Tactile sensing can enable a robot to infer properties of its surroundings,
such as the material of an object. Heat transfer based sensing can be used for
material recognition due to differences in the thermal properties of materials.
While data-driven methods have shown promise for this recognition problem, many
factors can influence performance, including sensor noise, the initial
temperatures of the sensor and the object, the thermal effusivities of the
materials, and the duration of contact. We present a physics-based mathematical
model that predicts material recognition performance given these factors. Our
model uses semi-infinite solids and a statistical method to calculate an F1
score for the binary material recognition. We evaluated our method using
simulated contact with 69 materials and data collected by a real robot with 12
materials. Our model predicted the material recognition performance of support
vector machine (SVM) with 96% accuracy for the simulated data, with 92%
accuracy for real-world data with constant initial sensor temperatures, and
with 91% accuracy for real-world data with varied initial sensor temperatures.
Using our model, we also provide insight into the roles of various factors on
recognition performance, such as the temperature difference between the sensor
and the object. Overall, our results suggest that our model could be used to
help design better thermal sensors for robots and enable robots to use them
more effectively.Comment: This article is currently under review for possible publicatio
Inferring Object Properties from Incidental Contact with a Tactile-Sensing Forearm
arXiv:1409.4972v1 [cs.RO]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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