Development of neural mechanisms for machine learning.
The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations - we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.
Arsenio
Artur M
AM
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, The Stata Center, 32 Vassar Street, Room G32-376, Cambridge, Massachusetts 02139, USA. arsenio@csail.mit.edu
D000465:Algorithms; D006801:Humans; D007858:Learning / Q000502:physiology*; D012371:Robotics / Q000379:methods*; D013663:Teaching / Q000379:methods*; D013664:Teaching Materials