Learning to Balance While Reaching: A Cerebellar-Based Control Architecture for a Self-balancing Robot
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 expl
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SPECS, Technology Department, Universitat Pompeu Fabra, Carrer de Roc Boronat 138, 08018 Barcelona, Spain [email protected], {ivan.herreros, giovanni.maffei,marti.sanchez,paul.verschure}@upf.edu 2 Department of Mechanical Engineering, Westfälische Hochschule, University of Applied Sciences, Bocholt, Gelsenkirchen, Germany 3 ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08010 Barcelona, Spain
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. Keywords: Cerebellar control Anticipatory Self-balancing robot Adaptive control
postural
adjustments
1 Introduction Nowadays self-balancing robots are becoming pervasive in everyday situations and there exist numerous prototypes that explore new configurations and application domains. However, most of the commercial self-balancing robots (SBRs) use solely Research supported by socSMC-641321—H2020-FETPROACT-2014. © Springer International Publishing Switzerland 2016 N.F. Lepora et al. (Eds.): Living Machines 2016, LNAI 9793, pp. 214–226, 2016. DOI: 10.1007/978-3-319-42417-0_20
Learning to Balance While Reaching
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pre-programmed feedback control [1]. In this paper we explore the use of adaptive control techniques in order to deal with a problem that will arise the moment SBRs are to include additional actuators. That is, how to adapt and prevent the momentary loss of balance that occurs once a SBR changes its body configuration by extending a manipulator to reach for objects, thereby modifying the position of its center of mass? Our approach to solve this problem is grounded in nature, namely in experimental psychology and neuroscience. On the one hand, humans and other bipedal primates are faced with the same difficulty when they reach for objects. They have to adapt their stance in order to preserve balance. Indeed,
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