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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
Perception01:28

Perception

Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...

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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
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Published on: April 16, 2014

Perception-action map learning in controlled multiscroll systems applied to robot navigation.

Paolo Arena1, Sebastiano De Fiore, Luigi Fortuna

  • 1Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Universitá degli Studi di Catania, viale A. Doria 6, 95125 Catania, Italy.

Chaos (Woodbury, N.Y.)
|January 7, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel robot perception technique using chaotic dynamics to learn environmental patterns for action. This bio-inspired approach enables robots to autonomously acquire task-relevant meanings for improved navigation.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Dynamical Systems

Background:

  • Perceptual states in robots can be encoded into chaotic attractors, linking sensory stimuli to dynamical evolution.
  • Environment-dependent patterns can be embedded within chaotic dynamics for robot control.
  • Action-oriented perception requires linking these patterns to robot actions for mission completion.

Purpose of the Study:

  • To close the action-oriented perception loop in robots.
  • To introduce an unsupervised learning stage for autonomous learning of perceptual meanings.
  • To develop a bio-inspired structure based on the motor map paradigm for robot control.

Main Methods:

  • Utilizing chaotic attractors to represent perceptual states and encode environment-dependent patterns.
  • Implementing an unsupervised learning stage based on the motor map paradigm.
  • Testing the framework on a simulated robot and a roving robot with Field Programmable Gate Array implementation.

Main Results:

  • Successfully demonstrated autonomous learning of perceptual meanings for task-specific navigation.
  • Compared performance favorably against traditional navigation control paradigms.
  • Reported preliminary experimental results on a physical roving robot.

Conclusions:

  • The proposed action-oriented perception technique effectively enables robots to autonomously learn and utilize environmental information for task execution.
  • The bio-inspired motor map paradigm integrated with chaotic dynamics offers a promising approach for intelligent robot behavior.
  • The framework shows potential for real-world robotic applications, as evidenced by simulation and preliminary hardware results.