A Framework for Learning Declarative Structure
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
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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.
- Numerical Mathematics