Abstract
One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.
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Martius, G., Jahn, L., Hauser, H., Hafner, V.V. (2014). Self-exploration of the Stumpy Robot with Predictive Information Maximization. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_4
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DOI: https://doi.org/10.1007/978-3-319-08864-8_4
Publisher Name: Springer, Cham
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