Accession Number:

ADA459921

Title:

A Framework for Learning Declarative Structure

Descriptive Note:

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE

Personal Author(s):

• Hart, Stephen
• Ou, Shichao
• Sweeney, John
• Grupen, Rod

Report Date:

2006-01-01

Pagination or Media Count:

6.0

Abstract:

This paper provides a framework with which a humanoid robot can efficiently learn complex behavior. In this framework, a robot is rewarded by learning how to generate novel sensorimotor feedback a form of native motivation. This intrinsic drive biases the robot to learn increasingly complex knowledge about itself and its effect on the environment. The framework includes a mechanism for uncovering hidden state in a well-structured state and action space. We present an example wherein the robot, Dexter, learns a sequence of manual skills 1 searching for and grasping an object, 2 the length of its arms, and 3 how to portray its intentions to human teachers in order to induce them to help.

Descriptors:

• *SKILLS
• *ROBOTS
• *LEARNING MACHINES
• ALGORITHMS
• CONTROL SYSTEMS
• SEQUENCES
• MANUAL OPERATION
• INSTRUCTORS
• CLOSED LOOP SYSTEMS
• SCHEMATIC DIAGRAMS

Subject Categories:

• Numerical Mathematics
• Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE