A decision method for the placement of mechanical tactile elements for grasp type recognition

The present paper describes a decision method for the placement of mechanical tactile elements for grasp type recognition. Based on the mutual information of the manipulation tasks and tactile information, an effective placement of tactile elements on a sensing glove is determined. Although the effective placement consists of a small number of tactile elements, it has a recognition performance that is as high as that of a placement consisting of many elements. The effective placement of tactile elements decided by the proposed method has been evaluated through experiments involving the recognition of grasp type from grasp taxonomy defined by Kamakura.

[1]  Makoto Shimojo,et al.  Development of a New Hand-Grasp Measurement System , 1995 .

[2]  Katsushi Ikeuchi,et al.  A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models , 2005, IEEE Transactions on Robotics.

[3]  J. R. Quinlan.  Discovering rules by induction from large collections of examples Intro-ductory readings in expert s , 1979 .

[4]  Stefano Caselli,et al.  Grasp recognition in virtual reality for robot pregrasp planning by demonstration , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[5]  R. Johansson,et al.  Tactile sensibility in the human hand: relative and absolute densities of four types of mechanoreceptive units in glabrous skin. , 1979, The Journal of physiology.

[6]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[7]  N. Kamakura,et al.  Patterns of static prehension in normal hands. , 1980, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[8]  Danica Kragic,et al.  Grasp Recognition for Programming by Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  R. Johansson,et al.  Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. , 1984, Human neurobiology.

[10]  Katsushi Ikeuchi,et al.  Generation of a task model by integrating multiple observations of human demonstrations , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[11]  Katsushi Ikeuchi,et al.  Toward automatic robot instruction from perception-mapping human grasps to manipulator grasps , 1997, IEEE Trans. Robotics Autom..

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[13]  Kian Hsiang Low,et al.  A Mapping Method for Telemanipulation of the Non-Anthropomorphic Robotic Hands with Initial Experimental Validation , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[14]  Tsukasa Ogasawara,et al.  Perception of human manipulation based on contact state transition , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[15]  Antonio Bicchi,et al.  Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity , 2000, IEEE Trans. Robotics Autom..