Abstract
In nature, Anticipatory Postural Adjustments (APAs) are actions that precede predictable disturbances with the goal of maintaining a stable body posture. Neither the structure of the computations that enable APAs are known nor adaptive APAs have been exploited in robot control. Here we propose a computational architecture for the acquisition of adaptive APAs based on current theories about the involvement of the cerebellum in predictive motor control. The architecture is applied to a simulated self-balancing robot (SBR) mounting a moveable arm, whose actuation induces a perturbation of the robot balance that can be counteracted by an APA. The architecture comprises both reactive (feedback) and anticipatory-adaptive (feed-forward) layers. The reactive layer consists of a cascade-PID controller and the adaptive one includes cerebellar-based modules that supply the feedback layer with predictive signals. We show that such architecture succeeds in acquiring functional APAs, thus demonstrating in a simulated robot an adaptive control strategy for the cancellation of a self-induced disturbance grounded in animal motor control. These results also provide a hypothesis for the implementation of APAs in nature that could inform further experimental research.
Research supported by supported by socSMC-641321—H2020-FETPROACT-2014.
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Acknowledgements
The research leading to these results has received funding from the European Commission’s Horizon 2020 socSMC project (under agreement number: socSMC-641321H2020-FETPROACT-2014) and by the European Research Council’s CDAC project: (ERC-2013-ADG 341196).
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Ruck, M., Herreros, I., Maffei, G., Sánchez-Fibla, M., Verschure, P. (2016). Learning to Balance While Reaching: A Cerebellar-Based Control Architecture for a Self-balancing Robot. In: Lepora, N., Mura, A., Mangan, M., Verschure, P., Desmulliez, M., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2016. Lecture Notes in Computer Science(), vol 9793. Springer, Cham. https://doi.org/10.1007/978-3-319-42417-0_20
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