Learning Bayes Filter Models for Tactile Localization
Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or manipulators with unknown world coordinates (e.g. poor camera-arm calibration) cannot locate the gripper with respect to...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
11.11.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Localizing and tracking the pose of robotic grippers are necessary skills for
manipulation tasks. However, the manipulators with imprecise kinematic models
(e.g. low-cost arms) or manipulators with unknown world coordinates (e.g. poor
camera-arm calibration) cannot locate the gripper with respect to the world. In
these circumstances, we can leverage tactile feedback between the gripper and
the environment. In this paper, we present learnable Bayes filter models that
can localize robotic grippers using tactile feedback. We propose a novel
observation model that conditions the tactile feedback on visual maps of the
environment along with a motion model to recursively estimate the gripper's
location. Our models are trained in simulation with self-supervision and
transferred to the real world. Our method is evaluated on a tabletop
localization task in which the gripper interacts with objects. We report
results in simulation and on a real robot, generalizing over different sizes,
shapes, and configurations of the objects. |
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DOI: | 10.48550/arxiv.2011.05559 |