Abstract
This paper addresses the problem of characterizing robot grasps for unmodeled objects. We propose a set of intrinsic object features that are computed from the object image and the geometry of the robot hand. These features are validated by feeding them to neural networks which are trained with experimental data obtained with a humanoid robot. The results suggest that our features are actually suitable for predicting the reliability of a grip.
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References
Barrett Technology Inc, http://www.barrett.com/.
A. Bicchi and V. Kumar, Robotic Grasping Configuration and Contact: A Review. In IEEE Intl. Conf. on Robotics and Automation, pages 348–353, 2000.
E. Chinellato. Robust Strategies for Selecting Vision-Based Planar Grasps of Unknown Objects with a Three-Finger Hand. MSc Dissertation, Division of Informatics, Univ. of Edinburgh, 2002, http://www3.uji.es/~eris/MScThesis.pdf.
E. Chinellato, R.B. Fisher, A. Morales and Á.P. del Pobil. Ranking Planar Grasp Configurations for a Three-Finger Hand. In IEEE Intl. Conf. on Robotics and Automation, 2003.
A. Morales, P.J. Sanz, and Á.P. del Pobil. Vision-based computation of three-finger grasps on unknown planar objects. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, pages 1693–1698, 2002.
A. Morales, P.J. Sanz, Á.P. del Pobil, and A.H. Fagg. An experiment in constraining vision-based finger contact selection with gripper geometry. In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, pages 1711–1716, 2002.
M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Proceedings of the IEEE Intl. Conf. on Neural Networks, 1993.
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Chinellato, E., Morales, A., Valero, P.S., Pobil, Á.P.d. (2003). Validation of Features for Characterizing Robot Grasps. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_25
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DOI: https://doi.org/10.1007/3-540-44869-1_25
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