Abstract
We present a Human-Robot-Collaboration (HRC) framework consisting of a hybrid human motion prediction approach together with a game theoretical action selection. In essence, the robot is required to predict the motions of the human co-worker, and to proactively decide on its actions. For our prediction framework, model-based human motion trajectories are learned by data-driven methods for efficient trajectory rollouts in which obstacles are also considered. We provide the reliability analysis of human trajectory predictions within a human-robot collaboration experimental setup. The HRC scenario is modeled as an iterative game to select the actions for the Human-Robot-Team (HRT) by finding the Nash Equilibrium of the game. Experimental evaluation shows how the proposed prediction approach can be successfully integrated into a game theory based action selection framework.
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References
Dinh, K.H., Oguz, O., Huber, G., Gabler, V., Wollherr, D.: An approach to integrate human motion prediction into local obstacle avoidance in close human-robot collaboration. In: International Workshop on Advanced Robotics and its Social Impacts (ARSO). IEEE, pp. 1–6 (2015)
Koppula, H.S., Saxena, A.: Anticipating human activities using object affordances for reactive robotic response. Trans. Pattern Anal. Mach. Intell. 38(1), 14–29 (2016)
Mainprice, J., Berenson, D.: Human-robot collaborative manipulation planning using early prediction of human motion. In: International Workshop on Intelligent Robots and Systems (IROS). IEEE, pp. 299–306 (2013)
Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5(7), 1688–1703 (1985)
Kawato, M.: Internal models for motor control and trajectory planning. Current Opin. Neurobiol. 9(6), 718–727 (1999)
Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)
Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)
Maeda, G., Ewerton, M., Lioutikov, R., Amor, H.B., Peters, J., Neumann, G.: Learning interaction for collaborative tasks with probabilistic movement primitives. In: International Conference on Humanoid Robots. IEEE, pp. 527–534 (2014)
Hawkins, K.P., Bansal, S., Vo, N.N., Bobick, A.F.: Anticipating human actions for collaboration in the presence of task and sensor uncertainty. In: International Conference on Robotics and Automation (ICRA). IEEE, pp. 2215–2222 (2014)
Nikolaidis, S., Lasota, P., Ramakrishnan, R., Shah, J.: Improved human-robot team performance through cross-training, an approach inspired by human team training practices. Int. J. Robot. Res. 34(14), 1711–1730 (2015)
Gabler, V., Stahl, T., Huber, G., Oguz, O., Wollherr, D.: A game-theoretic approach for adaptive action selection in close distance human-robot-collaboration. In: International Conference on Robotics and Automation (ICRA). IEEE (submitted, 2016)
Li, Y., Tee, K.P., Chan, W.L., Yan, R., Chua, Y., Limbu, D.K.: Role adaptation of human and robot in collaborative tasks. In: International Conference on Robotics and Automation (ICRA). IEEE, pp. 5602–5607 (2015)
Jarrassé, N., Charalambous, T., Burdet, E.: A framework to describe, analyze and generate interactive motor behaviors. PloS One 7(11), e49945 (2012)
Turnwald, A., Althoff, D., Wollherr, D., Buss, M.: Understanding human avoidance behavior: interaction-aware decision making based on game theory. Int. J. Soc. Robot. 8(2), 331–351 (2016)
Yazdani, M., Gamble, G., Henderson, G., Hecht-Nielsen, R.: A simple control policy for achieving minimum jerk trajectories. Neural Netw. 27, 74–80 (2012)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx
Grant, M., Boyd, S.: Graph implementations for nonsmooth convex programs. In: Blondel, V., Boyd, S., Kimura, H. (eds.) Recent Advances in Learning and Control. LNCIS, vol. 371, pp. 95–110. Springer, Heidelberg (2008). http://stanford.edu/~boyd/graph_dcp.html
Hoffmann, H., Pastor, P., Park, D.H., Schaal, S.: Biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. In: International Conference on Robotics and Automation (ICRA). IEEE, pp. 2587–2592 (2009)
Leyton-Brown, K., Shoham, Y.: Essentials of game theory: a concise multidisciplinary introduction. Synth. Lect. Artif. Intell. Mach. Learn. 2(1), 1–88 (2008)
Kruskall, J., Liberman, M.: The symmetric time warping algorithm: From continuous to discrete. In: Time Warps, String Edits and Macromolecules (1983)
Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
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Oguz, O.S., Gabler, V., Huber, G., Zhou, Z., Wollherr, D. (2017). Hybrid Human Motion Prediction for Action Selection Within Human-Robot Collaboration. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_26
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DOI: https://doi.org/10.1007/978-3-319-50115-4_26
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