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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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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