Mimesis model from partial observations for a humanoid robot

International Journal of Robotics Research

Volumn 29, Issue 1, 2010, Pages 60-80

  Lee, Dongheui ^a   Nakamura, Yoshihiko ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Imitation; Mimesis model; Partial observation; Proto symbol

Indexed keywords

EVALUATION FACTOR; HUMANOID ROBOT; MATCHING ERROR; MIMESIS MODEL; MOTION DATA; PARTIAL OBSERVATION; PRIOR KNOWLEDGE; WHOLE-BODY MOTION;

ANTHROPOMORPHIC ROBOTS;

EID: 75649084204     PISSN: 02783649     EISSN: 17413176     Source Type: Journal    
DOI: 10.1177/0278364909342282     Document Type: Article

References (58)

1. Agarwal, A. and Triggs, B. (2004). 3D human pose from silhouettes by relevance vector regression. IEEE Conference on Computer Vision and Pattern Recognition, pp. 882-888.
2. Asada, H. and Izumi, H. (1989). Automatic program generation from teaching data for the hybrid control of robots. IEEE Transactions on Robotics and Automation, 5 (2). 166-173.
3. Bakker, P. and Kuniyoshi, Y. (1996). Robot see, robot do: an overview of robot imitation. AISB96 Workshop: Learning in Robots and Animals, pp. 3-11.
4. Bentivegna, D.C. and Atkeson, C.G. (2000). Using primitives in learning from observation. IEEE-RAS International Conference on Humanoid Robots.
5. Bentivegna, D.C., Atkeson, C.G. and Cheng, G. (2006). Learning similar tasks from observation and practice. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4994-5000.
6. Billard, A. and Mataric, M.J. (2000). Betty: Robot, play with me! Robot: O.k. how do we play? Betty: You watch me and do like I do Look!. Workshop on Interactive Robotics and Entertainment.
7. Billard, A. and Mataric, M. J. (2001). Learning human arm movements by imitation: evaluation of biologically inspired connectionist architecture. Robotics and Autonomous Systems, 37: 145-160.
8. Blimes, J.A. (1997). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report ICSI-TR-97-021, University of Berkeley.
9. Breazeal, C. and Scassellati, B. (2002). Robots that imitate humans. Trends in Cognitive Science, 6 (11). 481-487.
10. Chai, J. and Hodgins, J. (2005). Performance animation from low-dimensional control signals. ACM Transactions on Graphics, 24 (3). 686-696.
11. Chomat, O., Martin, J. and Crowley, J.L. (2000). A probabilistic sensor for the perception and recognition of activities. European Conference on Computer Vision, pp. 487- 503.
12. Cohen, I., Tian, Q., Zhou, X.S. and Huang, T.S. (2002). Feature selection using principal feature analysis. International Conference on Information Processing.
13. Davis, J. and Bobick, A. (1997). The representation and recognition of action using temporal templates. Proceedings of Computer Vision and Pattern Recognition, pp. 928-934.
14. Deacon, T. (1997). The Symbolic Species: The Co-Evolution of Language and the Brain. New York, W.W. Norton and Company Inc.
15. Donald, M. (1991). Origins of the Modern Mind. Cambridge, MA, Harvard University Press.
16. Fod, A., Mataric, M.J. and Jenkins, O.C. (2000). Automated derivation of primitives for movement classification. IEEE-RAS International Conference on Humanoid Robots.
17. Gallese, V., Fadiga, L., Fogassi, L. and Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119: 593-609.
18. Hayes, G. and Demiris, J. (1994). A robot controller using learning by imitation. International Symposium on Intelligent Robotic Systems, pp. 198-204.
19. Hovland, G.E., Sikka, P. and McCarragher, B.J. (1996). Skill acquisition from human demonstration using a hidden Markov model. IEEE International Conference on Robotics and Automation, pp. 2706-2711.
20. Inamura, T., Nakamura, Y. and Toshima, I. (2004). Embodied symbol emergence based on mimesis theory. The International Journal of Robotics Research, 23 (4). 363-377.
21. Inamura, T., Tanie, H. and Nakamura, Y. (2003). From stochastic motion generation and recognition to geometric symbol development and manipulation. IEEE-RSJ International Conference on Humanoid Robots, 1b-02.
22. Janus, B. and Nakamura, Y. (2005). Unsupervised probabilistic segmentation of motion data for mimesis modeling. 12th IEEE International Conference on Advanced Robotics, pp. 411-417.
23. Kang, S.B. and Ikeuchi, K. (1995). A robot system that observes and replicates grasping tasks. IEEE International Conference on Computer Vision, pp. 1093-1099.
24. Kulic, D., Takano, W. and Nakamura, Y. (2008). Combining automated on-line segmentation and incremental clustering for whole body motions. IEEE International Conference on Robotics and Automation, pp. 2591-2598.
25. Kuniyoshi, Y. (1994). The science of imitation-towards physically and socially grounded intelligence. RWC Technical Report TR-94001.
26. Kuniyoshi, Y., Inaba, M. and Inoue, H. (1994). Learning by watching: extracting reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation, 10 (6). 799-822.
27. Kurihara, K., Hoshino, S., Yamane, K. and Nakamura, Y. (2002). Optical motion capture system with pan-tilt camera tracking and realtime data processing. IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1241- 1248.
28. Lee, D., Kunori, H. and Nakamura, Y. (2008). Association of whole body motion from tool knowledge for humanoid robots. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2867-2874.
29. Lee, D. and Nakamura, Y. (2007). Motion capturing from monocular vision by statistical inference based on motion database: vector field approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 617- 623.
30. Lee, D., Ott, C. and Nakamura, Y. (2009). Mimetic communication with impedance control for physical human-robot interaction. IEEE International Conference on Robotics and Automation, pp. 1535-1542.
31. Liu, G. and McMillan, L. (2006). Estimation of missing markers in human motion capture. Visual Computer, 22: 721-728. (Pubitemid 44371180)
32. Liu, G., Zhang, J., Wang, W. and McMillan, L. (2006). Human motion estimation from a reduced marker set. Proceedings of the 2006 Symposium on Interactive 3D Graphics and Games, pp. 35-42.
33. Mataric, M.J. (2000). Getting humanoids to move and imitate. IEEE Intelligent Systems, 15 (4). 18-24.
34. Miklosi, A. (1999). The ethological analysis of imitation. Biological Review, 74: 347-374.
35. Mori, T., Segawa, Y., Shimosaka, M. and Sato, T. (2004). Hierarchical recogntion of daily human actions based on continuous hidden Markov models. IEEE International Conference on Automatic Face and Gesture Recognition, TP1- 106.
36. Nicolescu, M. and Mataric, M. J. (2001). Learning and interacting in human-robot domains. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 31 (5). 419-430.
37. Ott, C., Lee, D. and Nakamura, Y. (2008). Motion capture based human motion recognition and imitation by direct marker control. IEEE-RAS International Conference on Humanoid Robots, pp. 399-405.
38. Rabiner, L.R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77 (2). 257-286.
39. Ramanan, D., Forsyth, D.A. and Zisserman, A. (2005). Strike a pose: tracking people by finding stylized poses. IEEE Conference on Computer Vision and Pattern Recognition, pp. 271-278.
40. Rizzolatti, G. and Craighero, L. (2004). The mirror-neuron system. Annual Reviews of Neuroscience, 27: 169-192.
41. Rizzolatti, G., Fadiga, L., Gallese, V. and Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3: 131-141. (Pubitemid 26094162)
42. Rosales, R. and Sclaroff, S. (2000). Inferring body pose without tracking body parts. Computer Vision and Pattern Recognition, pp. 721-727.
43. Samejima, K., Katagiri, K., Doya, K. and Kawato, M. (2002). Symbolization and imitation learning of motion sequence using competitive modules. Transactions of the Institute of Electronics, Information and Communication Engineers, J85-D-II(1): 90-100.
44. Schaal, S. (1997). Learning from demonstration. Advances in Neural Information Processing Systems, 9: 1040-1046.
45. Schaal, S. (1999). Is imitation learning the route to humanoid robots ? Trends in Cognitive Sciences, 3 (6). 233-242.
46. Schaal, S., Ijspeert, A. and Billard, A. (2003). Computational approaches to motor learning by imitation. Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358: 537-547.
47. Shon, A.P., Storz, J.J. and Rao, R.P. (2007). Towards a real-time Bayesian imitation system for a humanoid robot. IEEE International Conference on Robotics and Automation, pp. 2847-2852.
48. Stone, M. (1974). Cross-validation choice and assessment of statistical procedures. Journal of Royal Statistical Society, 36: 111-147.
49. Takano, W. and Nakamura, Y. (2005). Segmentation of human behavior patterns based on the probabilistic correlation. The 19th Annual Conference of the Japanese Society for the Artificial Intelligence, 3F1-02 (in Japanese).
50. Takano, W., Tanie, H. and Nakamura, Y. (2005). Key feature extraction for probabilistic categorization of human motion patterns. 12th IEEE International Conference on Advanced Robotics, pp. 424-430.
51. Takano, W., Yamane, K., Sugihara, T., Yamamoto, K. and Nakamura, Y. (2006). Primitive communication based on motion recognition and generation with hierarchical mimesis model. IEEE International Conference on Robotics and Automation, pp. 3602-3609.
52. Taylor, C.J. (2000). Reconstruction of articulated objects from point correspondences in a single uncalibrated image. Computer Vision and Image Understanding, 80: 677-684.
53. Tokuda, K., Zen, H. and Kitamura, T. (2003). Trajectory modeling based on hmms with the explicit relationship between static and dynamic features. European Conference on Speech Communication and Technology, pp. 1-4.
54. Umilta, M., Kohler, E., Gallese, V., Fogassi, L., and Fadiga, L. (2001). " I know what you are doing": a neurophysiological study. Neuron, 32: 91-101.
55. Yamane, K. (2004). Simulating and Generating Motions of Human Figures (Springer Tracts in Advanced Robotics, Vol 9). New York, Springer.
56. Yamane, K. and Nakamura, Y. (2003). Dyanmics filter- concept and implementation of on-line motion generator for human figures. IEEE Transactions on Robotics and Automation, 19 (3). 421-432.
57. Yamato, J., Ohya, J. and Ishii, K. (1992). Recognizing human action in time-sequential images using hidden Markov model. IEEE Conference on Computer Vision and Pattern Recognition, pp. 379-385.
58. Yang, J., Xu, Y. and Chen, C. (1997). Human action learning via hidden Markov model. IEEE Transactions on System, Man, and Cybernetics, 27 (1). 34-44.