Paper Info
Title
Learning to Balance While Reaching: A Cerebellar-Based Control Architecture for a Self-balancing Robot.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-42417-0_20
BIOMIMETIC AND BIOHYBRID SYSTEMS, LIVING MACHINES 2016
Keywords
Field
DocType
Cerebellar control,Anticipatory postural adjustments,Self-balancing robot,Adaptive control
Robot learning,Robot control,Control theory,Architecture,Computer science,Control theory,Motor control,Body posture,Adaptive control,Robot
Conference
Volume
ISSN
Citations 
9793
0302-9743
0
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Maximilian Ruck100.34
Ivan Herreros2358.13
Giovanni Maffei3133.62
Martí Sánchez-Fibla44411.13
Paul F. M. J. Verschure5677116.64