Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 11241))
Included in the following conference series:
1933Accesses
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.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprintarXiv:1412.6980 (2014)
Lee, S.H., Sifakis, E., Terzopoulos, D.: Comprehensive biomechanical modeling and simulation of the upper body. ACM Trans. Graph.28(4), 99:1–17 (2009)
Lee, S.H., Terzopoulos, D.: Heads up! biomechanical modeling and neuromuscular control of the neck. ACM Trans. Graph.23(212), 1188–1198 (2006)
Nakada, M., Chen, H., Terzopoulos, D.: Deep learning of biomimetic visual perception for virtual humans. In: ACM Symposium on Applied Perception (SAP 18), pp. 20:1–8. Vancouver, BC, August 2018
Nakada, M., Zhou, T., Chen, H., Weiss, T., Terzopoulos, D.: Deep learning of biomimetic sensorimotor control for biomechanical human animation. ACM Trans. Graph.37(4), 56:1–15 (2018). (in Proc. ACM SIGGRAPH 2018)
Rabie, T.F., Terzopoulos, D.: Active perception in virtual humans. In: Proceedings of Vision Interface 2000, pp. 16–22. Montreal, Canada (2000)
Schwartz, E.L.: Spatial mapping in the primate sensory projection: analytic structure and relevance to perception. Biol. Cybern.25(4), 181–194 (1977)
Shirley, P., Morley, R.K.: Realistic Ray Tracing, 2nd edn. A. K. Peters Ltd, Natick (2003)
Terzopoulos, D., Rabie, T.F.: Animat vision: active vision with artificial animals. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 840–845. Cambridge, MA (1995)
Yeo, S.H., Lesmana, M., Neog, D.R., Pai, D.K.: Eyecatch: simulating visuomotor coordination for object interception. ACM Trans. Graph. (TOG)31(4), 42 (2012)
Author information
Authors and Affiliations
University of California, Los Angeles, USA
Masaki Nakada, Honglin Chen & Demetri Terzopoulos
- Masaki Nakada
You can also search for this author inPubMed Google Scholar
- Honglin Chen
You can also search for this author inPubMed Google Scholar
- Demetri Terzopoulos
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toMasaki Nakada.
Editor information
Editors and Affiliations
University of Nevada, Reno, USA
George Bebis
NASA Ames Research Center, Moffett Field, USA
Richard Boyle
University of Nevada, Reno, USA
Bahram Parvin
Desert Research Institute, Reno, USA
Darko Koracin
DARPA, Arlington, USA
Matt Turek
University of Utah, Salt Lake City, USA
Srikumar Ramalingam
National University of Defense Technology, Changsha, China
Kai Xu
Microsoft Research Asia, Beijing, China
Stephen Lin
Bosch Research, Farmington Hills, MI, USA
Bilal Alsallakh
University of North Carolina at Charlotte, Charlotte, USA
Jing Yang
Microsoft Research, Redmond, USA
Eduardo Cuervo
University of Colorado at Colorado Springs, Colorado Springs, USA
Jonathan Ventura
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-03800-7
Online ISBN:978-3-030-03801-4
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative