Abstract
In this contribution, we consider learning tasks of a robot simulating a waiter in a restaurant. The robot records experiences and creates or adapts concepts represented in the web ontology language OWL 2, extended by quantitative spatial and temporal information. As a typical task, the robot is instructed to perform a specific activity in a few concrete scenarios and then expected to autonomously apply the conceptualized experiences to a new scenario. Constructing concepts from examples in a formal knowledge representation framework is well understood in principle, but several aspects important for realistic applications in robotics have remained unattended and are addressed in this paper. First, we consider conceptual representations of activity concepts combined with relevant factual knowledge about the environment. Second, the instructions can be coarse, confined to essential steps of a task, hence the robot has to autonomously determine the relevant context. Third, we propose a ”Good Common Subsumer” as opposed to the formal ”Least Common Subsumer” for the conceptualization of examples in order to obtain cognitively plausible results. Experiments are based on work in Project RACE where a PR2 robot is employed for recording experiences, learning and applying the learnt concepts.
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Neumann, B., Hotz, L., Rost, P., Lehmann, J. (2014). A Robot Waiter Learning from Experiences. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_22
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DOI: https://doi.org/10.1007/978-3-319-08979-9_22
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