Abstract
In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by home robots in real environments. For robots to act autonomously in a real environment, it is necessary to define appropriate actions for the environment. However, it is difficult to define these actions manually. Therefore, body motions that are common to multiple actions are defined as motion primitives. Complex actions can then be learned by combining these motion primitives. For learning motion primitives, we propose reference-point-dependent Gaussian process hidden semi-Markov model (RPD-GP-HSMM). For verification, a robot is tele-operated in order to perform actions included in several domestic household chores. The robot then learned the associated motion primitives from the robot’s body information and object information.
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Iwata, K., Nakamura, T., Nagai, T. (2019). Learning of Motion Primitives Using Reference-Point-Dependent GP-HSMM for Domestic Service Robots. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_35
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DOI: https://doi.org/10.1007/978-3-030-01370-7_35
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