Abstract
To efficiently facilitate autonomy sharing for assisting mobile robot teleoperation, in this paper we propose a method to recognize four contextual task types executed by the human operator: doorway crossing, object inspection, wall following and robot docking, which extends our previous approach, where only the first two task types were considered. We employ a set of simple but highly distinctive task features to efficiently describe each task type, which is adopted by a Gaussian Mixture Regression (GMR) model combined with a recursive Bayesian filter (RBF) to infer the most probable task the human operator executes across multiple candidates during operation. We demonstrate the effectiveness of the approach with a variety of tests in a cluttered indoor scenario.
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Gao, M., Schamm, T., Zöllner, J.M. (2015). Multiple Contextual Task Recognition for Sharing Autonomy to Assist Mobile Robot Teleoperation. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9245. Springer, Cham. https://doi.org/10.1007/978-3-319-22876-1_1
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DOI: https://doi.org/10.1007/978-3-319-22876-1_1
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