Grounding vision through experimental manipulation.
Experimentation is crucial to human progress at all scales, from society as a whole to a young infant in its cradle. It allows us to elicit learning episodes suited to our own needs and limitations. This paper develops active strategies for a robot to acquire visual experience through simple experimental manipulation. The experiments are oriented towards determining what parts of the environment are physically coherent--that is, which parts will move together, and which are more or less independent. 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.
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Paul
P
Fitzpatrick
Giorgio
G
Metta
D000222:Adaptation, Physiological / Q000502:physiology; D001185:Artificial Intelligence; D005246:Feedback; D009038:Motion; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009949:Orientation / Q000502:physiology; D010363:Pattern Recognition, Automated; D012371:Robotics / Q000379:methods*; D014785:Vision, Ocular / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*