Better Vision Through Manipulation

Abstract

For the purposes of manipulation, we would like to know what parts of the environment are physically coherent ensembles -- that is, which parts will move together, and which are more or less independent. It takes a great deal of experience before this judgement can be made from purely visual information. This paper develops active strategies for acquiring that experience through experimental manipulation, using tight correlations between arm motion and optic flow to detect both the arm itself and the boundaries of objects with which it comes into contact. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA434728

Entities

People

  • Giorgio Metta
  • Paul Fitzpatrick

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Boundaries
  • Brain
  • Cognitive Neuroscience
  • Cognitive Science
  • Computer Languages
  • Computer Vision
  • Computers
  • Control
  • Data Displays
  • Environment
  • Image Recognition
  • Neurons
  • Neurosciences
  • Object Recognition
  • Psychology
  • Reliability

Readers

  • Artificial Intelligence
  • Computer Vision.
  • Military Leadership and Professional Education.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy