Abstract
An interaction between a robot and a human could be difficult with only reactive mechanisms, especially in a social interaction, because the robot usually needs time to plan its movement. This paper discusses a motion generation system for humanoid robots to perform interactions with human motion prediction. To learn a human motion, a Long Short-Term Memory is trained using a public dataset. The effectiveness of the proposed technique is demonstrated by performing a handshake with a humanoid robot. Instead of following the human palm, the robot learns to predict the hand-meeting point. By using three metrics namely the smoothness, timeliness, and efficiency of the robot movements, the experimental results of various motion plans are compared. The predictive method shows a balanced trade-off point in all the metrics.
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Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A.: Collaborative human-humanoid carrying using vision and haptic sensing. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 607–612, May 2014
Avraham, G., Nisky, I., Fernandes, H.L., Acuna, D.E., Kording, K.P., Loeb, G.E., Karniel, A.: Toward perceiving robots as humans: three handshake models face the turing-like handshake test. IEEE Trans. Haptics 5(3), 196–207 (2012)
Bütepage, J., Black, M., Kragic, D., Kjellström, H.: Deep representation learning for human motion prediction and classification. arXiv e-prints, February 2017
De-Magistris, G., Micaelli, A., Evrard, P., Savin, J.: A human-like learning control for digital human models in a physics-based virtual environment. Vis. Comput. 31(4), 423–440 (2015)
Ewerton, M., Neumann, G., Lioutikov, R., Amor, H.B., Peters, J., Maeda, G.: Learning multiple collaborative tasks with a mixture of interaction primitives. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1535–1542, May 2015
Falahi, M., Shangari, T.A., Sheikhjafari, A., Gharghabi, S., Ahmadi, A., Ghidary, S.S.: Adaptive handshaking between humans and robots, using imitation: based on gender-detection and person recognition. In: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 936–941, October 2014
Flash, T., Hogans, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985)
Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4346–4354. IEEE Computer Society, Washington, DC (2015)
Huang, C.M., Mutlu, B.: Anticipatory robot control for efficient human-robot collaboration. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 83–90, March 2016
Inoue, T., De Magistris, G., Munawar, A., Yokoya, T., Tachibana, R.: Deep reinforcement learning for high precision assembly tasks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)
Jindai, M., Ota, S., Ikemoto, Y., Sasaki, T.: Handshake request motion model with an approaching human for a handshake robot system. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 265–270, July 2015
Jindai, M., Watanabe, T.: A handshake robot system based on a shake-motion leading model. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3330–3335, September 2008
Mainprice, J., Berenson, D.: Human-robot collaborative manipulation planning using early prediction of human motion. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 299–306, November 2013
Munawar, A., Vinayavekhin, P., De Magistris, G.: Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1017–1025, March 2017
Pham, T.H., Bufort, A., Caron, S., Kheddar, A.: Whole-body contact force sensing from motion capture. In: 2016 IEEE/SICE International Symposium on System Integration (SII), pp. 58–63. IEEE (2016)
Rohrmuller, F., Althoff, M., Wollherr, D., Buss, M.: Probabilistic mapping of dynamic obstacles using markov chains for replanning in dynamic environments. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2504–2510, September 2008
Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+D: a large scale dataset for 3D human activity analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 843–852. PMLR, Lille, 7–9 July 2015
Takamatsu, J., Morita, T., Ogawara, K., Kimura, H., Ikeuchi, K.: Representation for knot-tying tasks. IEEE Trans. Robot. 22(1), 65–78 (2006)
Wang, Q., Kurillo, G., Ofli, F., Bajcsy, R.: Evaluation of pose tracking accuracy in the first and second generations of Microsoft Kinect. In: 2015 International Conference on Healthcare Informatics, pp. 380–389, October 2015
Wang, Z., Giannopoulos, E., Slater, M., Peer, A., Buss, M.: Handshake: realistic human-robot interaction in haptic enhanced virtual reality. Presence Teleoper. Virtual Environ. 20(4), 371–392 (2011)
Zeng, Y., Li, Y., Xu, P., Ge, S.S.: Human-robot handshaking: a hybrid deliberate/reactive model. In: Ge, S.S., Khatib, O., Cabibihan, J.-J., Simmons, R., Williams, M.-A. (eds.) ICSR 2012. LNCS, vol. 7621, pp. 258–267. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34103-8_26
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Vinayavekhin, P. et al. (2017). Human-Like Hand Reaching by Motion Prediction Using Long Short-Term Memory. In: Kheddar, A., et al. Social Robotics. ICSR 2017. Lecture Notes in Computer Science(), vol 10652. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_16
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DOI: https://doi.org/10.1007/978-3-319-70022-9_16
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