Hybrid Human Motion Prediction for Action Selection Within Human-Robot Collaboration

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.