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Biomimetic Perception Learning for Human Sensorimotor Control

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

We present a simulation framework for biomimetic human perception and sensorimotor control. It features a biomechanically simulated, musculoskeletal human model actuated by numerous skeletal muscles, with two human-like eyes whose retinas have spatially nonuniform distributions of photoreceptors. Our prototype sensorimotor system for this model incorporates a set of 20 automatically-trained, deep neural networks (DNNs), half of which are neuromuscular DNN controllers comprising its motor subsystem, while the other half are devoted to visual perception. Within the sensory subsystem, which continuously operates on the retinal photoreceptor outputs, 2 DNNs drive eye and head movements, while 8 DNNs extract the sensory information needed to control the arms and legs. Exclusively by means of its egocentric, active visual perception, our biomechanical virtual human learns efficient, online visuomotor control of its eyes, head, and four limbs to perform tasks involving the foveation and visual pursuit of target objects coupled with visually-guided reaching actions to intercept the moving targets.

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Author information

Authors and Affiliations

  1. University of California, Los Angeles, USA

    Masaki Nakada, Honglin Chen & Demetri Terzopoulos

Authors
  1. Masaki Nakada

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  2. Honglin Chen

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  3. Demetri Terzopoulos

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Corresponding author

Correspondence toMasaki Nakada.

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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© 2018 Springer Nature Switzerland AG

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Nakada, M., Chen, H., Terzopoulos, D. (2018). Biomimetic Perception Learning for Human Sensorimotor Control. In: Bebis, G.,et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_7

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Chapter
JPY 3498
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  • Available as PDF
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  • Instant download
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eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
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Tax calculation will be finalised at checkout

Purchases are for personal use only


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