Building Subjective Spatial Perception Based on Sensor Space Integration for Motion Generation
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1
Tokyo University of Agriculture and Technology, Japan
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2
Tohoku Gakuin University, Japan
For the development of flexible intelligence of
robots, it is important to build a subjective spatial representation
in a bottom-up way. Humanoid robots as well as
humans have multi-modal sensors that cover wide range but
not necessarily overlap with each other. This paper presents
a motion generation method for robot with multiple sensors
with limited sensing ranges. The proposed method introduce
extension of the action-observation mapping to outside of the
sensing range of a sensor, based on the diffusion-based learning
of Jacobian matrices between control input and observation
variable. Multiple observation spaces can be integrated by finding
correspondence between the virtual observation spaces. The
proposed framework is verified by two robot tasks, reaching
motion toward the floor with a manipulator and navigation of
mobile robot around the wall. An implementation issue is also
discussed using a humanoid robot, where SIFT features are
utilized to recognize the robot’s body including perception of
depth information.
Keywords:
Diffusion-based learning,
Integration of sensor spaces,
Motion generation,
Multiple sensors,
spatial perception
Conference:
IEEE ICDL-EPIROB 2011, Frankfurt, Germany, 24 Aug - 27 Aug, 2011.
Presentation Type:
Poster Presentation
Topic:
Development principles
Citation:
Kobayashi
Y,
Kurita
E and
Gouko
M
(2011). Building Subjective Spatial Perception Based on Sensor Space Integration for Motion Generation.
Front. Comput. Neurosci.
Conference Abstract:
IEEE ICDL-EPIROB 2011.
doi: 10.3389/conf.fncom.2011.52.00015
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Received:
01 Jul 2011;
Published Online:
12 Jul 2011.
*
Correspondence:
Dr. Yuichi Kobayashi, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan, yu-koba@cc.tuat.ac.jp