Dynamic grasp recognition using time clustering, gaussian mixture models and hidden markov models

Advanced Robotics

Volumn 23, Issue 10, 2009, Pages 1359-1371

  Ju, Zhaojie ^a   Liu, Honghai ^a   Zhu, Xiangyang ^b   Xiong, Youlun ^c  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

GAUSSIAN MIXTURE MODELS; GRASP RECOGNITION; HIDDEN MARKOV MODELS; PROGRAMMING BY DEMONSTRATION; TIME CLUSTERING

Indexed keywords

EID: 85019442407     PISSN: 01691864     EISSN: 15685535     Source Type: Journal    
DOI: 10.1163/156855309X462628     Document Type: Article

References (22)

1. Bicchi, A., 2000. Hands for dexterous manipulation and robust grasping:a difficultroad toward simplicity. IEEE Trans. Robotics Automat., 16:652–662.
2. Zhu, X., and Ding, H., 2007. An efficient algorithm for grasp synthesis and fixture layout design in discrete domain. IEEE Trans. Robotics Automat., 23:157–163.
3. Xiong, C., Ding, H., and Xiong, Y., 2008. Fundamentals of Robotic Grasping and Fixturing, Singapore:World Scientific.
4. Zoliner, R., Pardowitz, M., Knoop, S., and Dillmann, R., 2005. “ Towards cognitive robots:building hierarchical task representations of manipulations from human demonstration ”. In Proc. IEEE Int. Conf. on Robotics and Automation 1535–1540. Barcelona
5. Palm, R., and Iliev, B., 2007. “ Segmentation and recognition of human grasps for programming-bydemonstration using time-clustering and fuzzy modeling ”. In Proc. IEEE Fuzzy Systems Conf 1–6. London
6. Robertsson, L., Iliev, B., Palm, R., and P.Wide. 2007. Perception modeling for human-like artificial sensor systems. Int. J. Human?Comp. Stud., 65:446–459.
7. Bernardin, K., Ogawara, K., Ikeuchi, K., and Dillmann, R., 2003. “ A hidden Markov model based sensor fusion approach for recognizing continuous human grasping sequences ”. In Proc. 3rd IEEE Int. Conf. on Humanoid Robots Munich
8. Bae, J., Arimoto, S., Yamamoto, Y., Hashiguchi, H., and Sekimoto, M., 2006. “ Reaching to grasp and preshaping of multi-DOFs robotic hand?arm systems using approximate configuration of objects ”. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 1605–1610. Beijing
9. Wu, Y., Lin, J., and Huang, T., 2001. “ Capturing natural hand articulation ”. In Proc. Int. Conf. on Computer Vision 426–432. Vancouver, BC
10. Dragulescu, D., and Ungureanu, L., 2007. “ The modeling process of a human hand prosthesis ”. In Proc. 4th Int. Symp. on Applied Computational Intelligence and Informatics 263–268. Timisoara
11. Bernardin, K., Ogawara, K., Ikeuchi, K., and Dillmann, R., 2005. A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models. IEEE Trans. Robotics Automat., 21:47–57.
12. Fels, S., and Hinton, G., 1993. Glove-talk:a neural network interface between a data-glove and a speech synthesizer. IEEE Trans. Neural Networks, 40:2–8.
13. Fels, S., and Hinton, G., 1998. Glove-TalkII—a neural-network interface which maps gestures to parallel formant speech synthesizer controls. IEEE Trans. Neural Networks, 9:205–212.
14. Chen, Y., and Tseng, K., 2007. “ Multiple-angle hand gesture recognition by fusing SVM classifiers ”. In Proc. IEEE Int. Conf. on Automation Science and Engineering 527–530. Scottsdale, AZ
15. Stern, H., Wachs, J., and Edan, Y., 2006. “ Human factors for design of hand gesture human?machine interaction ”. In Proc. IEEE Int. Conf. on Systems, Man and Cybernetics vol. 5, 4052–4056. Montreal, Que
16. Natal, R., Dimuro, B., and Costa, A., 2007. Hand gesture recognition in an interval fuzzy approach. TEMA Tend. Math. Appl. Comput., 8:21–31.
17. Bedregal, B., Costa, A., and Dimuro, G., 2006. Fuzzy rule-based hand gesture recognition. Artif. Intell. Theor. Practice, 217:285–294.
18. Heumer, G., Amor, H., Weber, M., and Jung, B., 2007. “ Grasp recognition with uncalibrated data gloves — a comparison of classification methods ”. In Proc. Virtual Reality Conf 19–26. Charlotte, NC
19. Wu, S., and Li, Y., 2008. On signature invariants for effective motion trajectory recognition. Int. J. Robotics Res., 27:895–917.
20. Iberall, T., 1997. Human prehension and dexterous robot hands. Int. J. Robotics Res., 16:285–299.
21. Liu, H., 2008. A fuzzy qualitative framework for connecting robot qualitative and quantitative representations. IEEE Trans. Fuzzy Syst., 16:1522–1530.
22. Liu, H., Brown, D., and Coghill, G., 2008. Fuzzy qualitative robot kinematics. IEEE Trans. Fuzzy Syst., 16:802–822.