Segmentation method of human manipulation task based on measurement of force imposed by a human hand on a grasped object

This paper proposes a segmentation method of human manipulation task based on measurement of contact force imposed by a human hand on a grasped object. We define an index measure for segmenting a human manipulation task into primitives. The indices are calculated from the set of the contact forces measured at all the contact points during a manipulation task. Then, we apply the EM algorithm to the set of the indices in order to segment the manipulation task into primitives. These primitives are mapped onto the robotic hand to impose appropriate contact forces on a grasped object. In the experiments, manipulation tasks performed in daily human life have been successfully segmented.

[1]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[2]  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.

[3]  Jr. J. Kenneth Salisbury,et al.  Kinematic and force analysis of articulated hands , 1982 .

[4]  J. K. Salisbury,et al.  Kinematic and Force Analysis of Articulated Mechanical Hands , 1983 .

[5]  Jiawei Hong,et al.  Calibrating a VPL DataGlove for teleoperating the Utah/MIT hand , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

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

[7]  Masayuki Inaba,et al.  Learning by watching: extracting reusable task knowledge from visual observation of human performance , 1994, IEEE Trans. Robotics Autom..

[8]  Katsushi Ikeuchi,et al.  Toward an assembly plan from observation. I. Task recognition with polyhedral objects , 1994, IEEE Trans. Robotics Autom..

[9]  Kostas J. Kyriakopoulos,et al.  Kinematic analysis and position/force control of the Anthrobot dextrous hand , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Peter K. Allen,et al.  Examples of 3D grasp quality computations , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

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

[12]  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).

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

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

[15]  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..

[16]  Tom M. Mitchell,et al.  Feature selection for grasp recognition from optical markers , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.