Abstract
We present a novel approach for fast prediction of human reaching motion in the context of human-robot collaboration in manipulation tasks. The method trains a recurrent neural network to process the three-dimensional hand trajectory and predict the intended target along with its certainty about the position. The network then updates its estimate as it receives more observations while advantaging the positions it is more certain about. To assess the proposed algorithm, we build a library of human hand trajectories reaching targets on a fine grid. Our experiments show the advantage of our algorithm over the state of the art in terms of classification accuracy.
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References
Lasota, P.A., Fong, T., Shah, J.A., et al.: A survey of methods for safe human-robot interaction. Found. Trends Robot. 5(4), 261–349 (2017)
Broquere, X., Sidobre, D., Herrera-Aguilar, I.: Soft motion trajectory planner for service manipulator robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 2808–2813 (2008)
Calinon, S., Sardellitti, I., Caldwell, D.G.: Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 249–254 (2010)
Dhillon, B., Fashandi, A., Liu, K.: Robot systems reliability and safety: a review. J. Qual. Maint. Eng. 8(3), 170–212 (2002)
Shi, J., Jimmerson, G., Pearson, T., Menassa, R.: Levels of human and robot collaboration for automotive manufacturing. In: Proceedings of the Workshop on Performance Metrics for Intelligent Systems, PerMI, pp. 95–100 (2012)
Jung, E.S., Park, S.: Prediction of human reach posture using a neural network for ergonomic man models. Comput. Ind. Eng. 27(1–4), 369–372 (1994)
Mainprice, J., Berenson, D.: Human-robot collaborative manipulation planning using early prediction of human motion. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 299–306 (2013)
Pérez-D’Arpino, C., Shah, J.A.: Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. In: Proceedings of the International Conference on Robotics and Automation, ICRA, pp. 6175–6182 (2015)
Kruskal, J., Liberman, M.: The Symmetric Time-Warping Problem: From Continuous to Discrete. Addison-Wesley, Reading (1983)
Maeda, G., Maloo, A., Ewerton, M., Lioutikov, R., Peters, J.: Anticipative interaction primitives for human-robot collaboration. In: Proceedings of the AAAI Fall Symposium Series, pp. 325–330 (2016)
Mainprice, J., Hayne, R., Berenson, D.: Predicting human reaching motion in collaborative tasks using inverse optimal control and iterative re-planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 885–892 (2015)
Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.: STOMP: stochastic trajectory optimization for motion planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 4569–4574 (2011)
Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S.: Learning objective functions for manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 1331–1336 (2013)
Dong, S., Williams, B.: Learning and recognition of hybrid manipulation motions in variable environments using probabilistic flow tubes. Int. J. Soc. Robot. 4(4), 357–368 (2012)
De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000)
Acknowledgments
The authors would like to thank Nuo Zhou for her assistance in developing the software and collecting the data.
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Hamandi, M., Hatay, E., Fazli, P. (2018). Predicting the Target in Human-Robot Manipulation Tasks. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_57
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DOI: https://doi.org/10.1007/978-3-030-05204-1_57
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