Tool-body assimilation model considering grasping motion through deep learning

Robotics and Autonomous Systems

Volumn 91, Issue , 2017, Pages 115-127

  Takahashi, Kuniyuki ^a   Kim, Kitae ^a   Ogata, Tetsuya ^a   Sugano, Shigeki ^a  

^a WASEDA UNIVERSITY   (Japan)

Author keywords

Deep neural network; Motor babbling; Recurrent neural network; Tool body assimilation; Transfer learning

Indexed keywords

DEEP LEARNING; DEEP NEURAL NETWORKS; RECURRENT NEURAL NETWORKS;

BODY MODELING; DIFFERENT SHAPES; INITIAL STATE; MODEL EVALUATION; TARGET IMAGES; TOOL USE; TRANSFER LEARNING;

ROBOTS;

EID: 85015064478     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2017.01.002     Document Type: Article

References (42)

1. [1] Beetz, M., Klank, U., Kresse, I., Maldonado, A., Mosenlechner, L., Pangercic, D., Ruhr, T., Tenorth, M., Robotic roommates making pancakes. IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2011, IEEE, 529–536.
2. [2] Ramirez-Amaro, K., Dean-Leon, E., Cheng, G., Robust semantic representations for inferring human co-manipulation activities even with different demonstration styles. IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 2015, IEEE, 1141–1146.
3. [3] Sugaiwa, T., Takahashi, K., Kano, H., Iwata, H., Sugano, S., Handling and grasp control with additional grasping point for dexterous manipulation of cylindrical tool. IEEE International Conference on Robotics and Biomimetics, 2011, 733–738.
4. [4] Nagahama, K., Yamazaki, K., Okada, K., Inaba, M., Manipulation of multiple objects in close proximity based on visual hierarchical relationships. IEEE International Conference on Robotics and Automation, 2013, 1303–1310.
5. [5] Kobayashi, Y., Hosoe, S., Planning-space shift motion generation: Variable-space motion planning toward flexible extension of body schema. J. Intell. Robot. Syst. 62:3–4 (2011), 467–500.
6. [6] Stoytchev, A., Behavior-grounded representation of tool affordances. IEEE International Conference on Robotics and Automation, 2005, 3060–3065.
7. [7] Maravita, A., Iriki, A., Tools for the body (schema). Trends Cogn. Sci. 8:2 (2004), 79–86.
8. [8] Nabeshima, C., Kuniyoshi, Y., Lungarella, M., Towards a model for tool-body assimilation and adaptive tool-use. IEEE International Conference on Development and Learning, ICDL, 2007, IEEE, 288–293.
9. [9] Nishide, S., Tani, J., Takahashi, T., Okuno, H.G., Ogata, T., Tool–body assimilation of humanoid robot using a neurodynamical system. IEEE Trans. Auton. Ment. Dev. 4:2 (2012), 139–149.
10. [10] Yamaguchi, Y., Nobuta, H., Nishide, S., Okuno, H.G., Ogata, T., Tool-body assimilation model using neuro-dynamical system for acquiring representation of tool function. Proceedings of IROS-2012 Workshop on Cognitive Neuroscience Robotics, 2012, PM2–3, 6 pages.
11. [11] Takahashi, K., Ogata, T., Tjandra, H., Yamaguchi, Y., Sugano, S., Tool-body assimilation model based on body babbling and neurodynamical system. Math. Probl. Eng., 2015, 2015, 10.1155/2015/837540.
12. [12] J.J. Gibson, The senses considered as perceptual systems, 1966.
13. [13] Streri, A., Féron, J., The development of haptic abilities in very young infants: from perception to cognition. Infant Behav. Dev. 28:3 (2005), 290–304.
14. [14] Michaels, C.F., Weier, Z., Harrison, S.J., Using vision and dynamic touch to perceive the affordances of tools. Perception 36:5 (2007), 750–772.
15. [15] Sturm, J., Plagemann, C., Burgard, W., Unsupervised body scheme learning through self-perception. IEEE International Conference on Robotics and Automation, 2008, 3328–3333.
16. [16] Saegusa, R., Metta, G., Sandini, G., Sakka, S., Active motor babbling for sensorimotor learning. IEEE International Conference on Robotics and Biomimetics, 2009, 794–799.
17. [17] Saegusa, R., Metta, G., Sandini, G., Natale, L., Developmental perception of the self and action. IEEE Trans. Neural Netw. Learn. Syst., 2014.
18. [18] Rolf, M., Steil, J.J., Gienger, M., Goal babbling permits direct learning of inverse kinematics. IEEE Trans. Auton. Mental Dev. 2:3 (2010), 216–229.
19. [19] Rolf, M., Steil, J.J., Efficient exploratory learning of inverse kinematics on a bionic elephant trunk. IEEE Trans. Neural Netw. Learn. Syst. 25:6 (2014), 1147–1160.
20. [20] Mochizuki, K., Nishide, S., Okuno, H.G., Ogata, T., Developmental human-robot imitation learning of drawing with a neuro dynamical system. IEEE International Conference on Systems, Man, and Cybernetics, 2013, 2336–2341.
21. [21] Head, H., Holmes, G., Sensory disturbances from cerebral lesions. Brain 34:2–3 (1911), 102–254.
22. [22] Sasaoka, T., Mizuhara, H., Inui, T., The interaction between the parietal and motor areas in dynamic imagery manipulation: an fmri study. Advances in Cognitive Neurodynamics (II), 2011, Springer, 345–349.
23. [23] Carpenter, M., The co-ordination and regulation of movements. J. Neuropathol. Exp. Neurol., 27(2), 1968, 348.
24. [24] Wolpert, D.M., Kawato, M., Multiple paired forward and inverse models for motor control. Neural Netw. 11:7 (1998), 1317–1329.
25. [25] Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks. Science 313:5786 (2006), 504–507.
26. [26] Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 2012, 1097–1105.
27. [27] Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29:6 (2012), 82–97.
28. [28] Noda, K., Arie, H., Suga, Y., Ogata, T., Multimodal integration learning of robot behavior using deep neural networks. Robot. Auton. Syst. 62:6 (2014), 721–736.
29. [29] Funabashi, S., Schmitz, A., Sato, T., Somlor, S., Sugano, S., Robust in-hand manipulation of variously sized and shaped objects. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2015, IEEE, 257–263.
30. [30] Sasaki, K., Tjandra, H., Noda, K., Takahashi, K., Ogata, T., Neural network based model for visual-motor integration learning of robot's drawing behavior: Association of a drawing motion from a drawn image. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2015, IEEE, 2736–2741.
31. [31] S. Levine, C. Finn, T. Darrell, P. Abbeel, End-to-end training of deep visuomotor policies, ArXiv Preprint, 2015. arXiv:1504.00702.
32. [32] S. Levine, P. Pastor, A. Krizhevsky, D. Quillen, Learning hand–eye coordination for robotic grasping with deep learning and large-scale data collection, ArXiv Preprint, 2016. arXiv:1603.02199.
33. [33] Martens, J., Deep learning via hessian-free optimization. Proceedings of the 27th International Conference on Machine Learning, ICML-10, 2010, 735–742.
34. [34] Pearlmutter, B.A., Fast exact multiplication by the hessian. Neural Comput. 6:1 (1994), 147–160.
35. [35] Schraudolph, N.N., Fast curvature matrix-vector products for second-order gradient descent. Neural Comput. 14:7 (2002), 1723–1738.
36. [36] Yamashita, Y., Tani, J., Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Comput. Biol., 4(11), 2008, e1000220.
37. [37] Elman, J.L., Finding structure in time. Cogn. Sci. 14:2 (1990), 179–211.
38. [38] Williams, D.R.G.H.R., Hinton, G.E., Learning representations by back-propagating errors. Nature 323:6088 (1986), 533–538.
39. [39] Pan, S.J., Yang, Q., A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22:10 (2010), 1345–1359.
40. [40] Kokoro Company Ltd., Kokoro: Custom-made robot: ACTROID, 2016. http://www.kokoro-dreams.co.jp/english/rt_tokutyu/actroid.html. (Accessed 2 February 2016).
41. [41] Kanehiro, F., Hirukawa, H., Kajita, S., OpenHRP: Open architecture humanoid robotics platform. Int. J. Robot. Res. 23:2 (2004), 155–165.
42. [42] Kapandji, I.A., The Physiology of the Joints: Upper Limb. 2007, Churchill Livingstone.