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Perceptual Robotics

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Springer Handbook of Robotics

Abstract

Perceptual functions are central to many applications in robotics and for the construction of efficient human–robot interfaces. The study of perception in biological systems has revealed important information-processing principles that have been converted to powerful applications in robotics and computer vision. The chapter first discusses two central theories of object recognition: model- and exemplar-based theories. A review of experimental results from the study of object recognition in biological systems suggests that exemplar-based approaches capture important properties of object recognition in the brain. We then discuss how very similar principles have been realized in highly efficient technical systems for object recognition and detection, including realizations that are based on biologically inspired neural architectures. Principles for the efficient processing of complex shapes can be extended to the representation of complex movements and actions. We illustrate this by first reviewing some properties of the cortical mechanisms of the recognition of complex movements and actions, focusing on principles that are useful for robotics applications. Again, exemplar-based approaches seem to capture important properties of motion recognition in the brain, and at the same time provide a powerful approach for building technical movement recognition systems. Finally, it is shown that the example-based framework is not only useful for recognition, but also provides the basis for powerful synthesis methods. As one example we discuss the synthesis of photorealistic three-dimensional (3-D) models of faces, exploiting correspondencebetween training examples. Related approaches have been developed for spatiotemporal patterns. We review a class of algorithms that permit the accurate modeling of movements and movement styles by interpolation between example trajectories with high relevance for the synthesis of movements, e.g., in humanoid robotics.

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Abbreviations

AIP:

anterior interparietal area

AIT:

anterior inferotemporal cortex

EBA:

extrastriate body part area

GSD:

geon structural description

HMM:

hidden Markov model

IT:

inferotemporal

IT:

intrinsic tactile

LGN:

lateral geniculate nucleus

MT:

medial temporal

MT:

multitask

NAP:

nonaccidental properties

PCA:

principle components analysis

PFC:

prefrontal cortex

PIT:

posterior inferotemporal cortex

RBC:

recognition-by-components

RBF:

radial basis function

RT:

reaction time

RT:

room-temperature

STS:

superior temporal sulcus

ZMP:

zero-moment point

fMRI:

functional magnetic resonance imaging

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Authors and Affiliations

  1. Kognitive Humanpsychophysik, Max-Planck-Institut für biologische Kybernetik, Spemannstr. 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff Prof

  2. Kognitive Humanpsychophysik, Max-Planck-Institut für biologische Kybernetic, Spemannstr. 38, 72076, Tübingen, Germany

    Christian Wallraven PhD

  3. Department of Psychology, University of Wales, Penrallt Rd., LL 57 2AS, Bangor, UK

    Martin A. Giese PhD

Authors
  1. Heinrich H. Bülthoff Prof
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  2. Christian Wallraven PhD
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  3. Martin A. Giese PhD
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Corresponding authors

Correspondence to Heinrich H. Bülthoff Prof , Christian Wallraven PhD or Martin A. Giese PhD .

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Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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Bülthoff, H.H., Wallraven, C., Giese, M.A. (2008). Perceptual Robotics. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_64

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