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Related Concept Videos

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

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Active vision and receptive field development in evolutionary robots.

Dario Floreano1, Mototaka Suzuki, Dario Mattiussi

  • 1Laboratory of Intelligent Systems, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland. Dario.Floreano@epfl.ch

Evolutionary Computation
|November 22, 2005
PubMed
Summary
This summary is machine-generated.

Artificial evolution enhanced mobile robot adaptability. Robots with Hebbian visual plasticity in simulations showed improved real-world performance, demonstrating effective active vision strategies.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Mobile robots require adaptive control systems for dynamic environments.
  • Neural controllers map visual input to motor commands.
  • Online evolution and Hebbian plasticity offer mechanisms for adaptation.

Purpose of the Study:

  • To artificially evolve adaptive neural controllers for an outdoor mobile robot with a mobile camera.
  • To investigate the role of Hebbian plasticity in visual receptive field formation.
  • To compare the robustness of evolved controllers in simulation versus real-world environments.

Main Methods:

  • Utilized a genetic algorithm for online evolution of the neural control system.
  • Incorporated Hebbian plasticity for modifying synaptic connections (receptive fields).
  • Employed physics-based simulations and real-world outdoor testing.

Main Results:

  • Robots evolved with Hebbian visual plasticity exhibited more robust adaptive behavior in real environments.
  • Active vision significantly influenced receptive field formation compared to static image sampling.
  • The interplay of active vision and receptive field formation led to the selection of a consistent subset of visual features.

Conclusions:

  • Hebbian visual plasticity enhances the robustness of evolved neural controllers for mobile robots.
  • Active vision plays a crucial role in shaping visual receptive fields for adaptive behavior.
  • Robots can learn to exploit specific visual features through the combined mechanisms of evolution and plasticity.