A decision method for the placement of tactile sensors for manipulation task recognition

The present paper describes a decision method for the placement of tactile elements for manipulation task 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 tactile 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 et al., (1980).

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

[2]  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).

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

[4]  Haruhisa Kawasaki,et al.  Educational–industrial complex development of an anthropomorphic robot hand 'Gifu hand' , 2001, Adv. Robotics.

[5]  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.

[6]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[7]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[8]  Masamichi Shimosaka,et al.  Hierarchical recognition of daily human actions based on Continuous Hidden Markov Models , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  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.

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

[11]  J. Napier The prehensile movements of the human hand. , 1956, The Journal of bone and joint surgery. British volume.

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

[13]  '. B.Vallbo Properties of cutaneous mechanoreceptors in the human hand-related to touch sensation , 1999 .

[14]  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).

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

[16]  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..

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

[18]  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.

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