IMIS

Publications | Institutes | Persons | Datasets | Projects | Maps | Infrastructure
[ report an error in this record ]basket (0): add | show Print this page

Neurorobotics: from vision to action
van der Smagt, P.; Arbib, M.A.; Metta, G. (2016). Neurorobotics: from vision to action, in: Siciliano, B. et al. Springer handbook of robotics. pp. 2069-2094. https://dx.doi.org/10.1007/978-3-319-32552-1_77
In: Siciliano, B.; Khatib, O. (Ed.) (2016). Springer handbook of robotics. Second edition. Springer Verlag: Berlin. ISBN 978-3-319-32550-7; e-ISBN 978-3-319-32552-1. LXXVI, 2227 pp. https://dx.doi.org/10.1007/978-3-319-32552-1, more

Available in  Authors 

Authors  Top 
  • van der Smagt, P.
  • Arbib, M.A.
  • Metta, G.

Abstract
    The lay view of a robot is a mechanical human, and thus robotics has always been inspired by attempts to emulate biology. In this chapter, we extend this biological motivation from humans to animals more generally, but with a focus on the central nervous systems in its relationship to the bodies of these creatures. In particular, we investigate the sensorimotor loop in the execution of sophisticated behavior. Some of these sections concentrate on cases where vision provides key sensory data. Neuroethology is the study of the brain mechanisms underlying animal behavior, and Sect. 77.2 exemplifies the lessons it has to offer robotics by looking at optic flow in bees, visually guided behavior in frogs, and navigation in rats, turning then to the coordination of behaviors and the role of attention. Brains are composed of diverse subsystems, many of which are relevant to robotics, but we have chosen just two regions of the mammalian brain for detailed analysis. Section 77.3 presents the cerebellum. While we can plan and execute actions without a cerebellum, the actions are no longer graceful and become uncoordinated. We reveal how a cerebellum can provide a key ingredient in an adaptive control system, tuning parameters both within and between motor schemas. Section 77.4 turns to the mirror system, which provides shared representations which bridge between the execution of an action and the observation of that action when performed by others. We develop a neurobiological model of how learning may forge mirror neurons for hand movements, provide a Bayesian view of a robot mirror system, and discuss what must be added to a mirror system to support robot imitation. We conclude by emphasizing that, while neuroscience can inspire novel robotic designs, it is also the case that robots can be used as embodied test beds for the analysis of brain models.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors