Theses and Dissertations Collection

In learning from demonstration (LfD) a human trainer demonstrates desired

behaviors to a robotic agent, creating a training set that the agent can learn from.

LfD allows non-programmers to easily and naturally train robotic agents to perform

specific tasks. However, to date most LfD has focused on single robot, single trainer

paradigms leading to bottlenecks in both the time required to demonstrate tasks and

the time required to learn behaviors. A previously untested approach to addressing

these limitations is to use distributed LfD with a distributed, evolutionary algorithm.

Distributed learning is a model for robust real world learning without the need for a

central computer. In the distributed LfD system presented here multiple trainers train

multiple robots on different, but related, tasks in parallel and each robot runs its own

on-board evolutionary algorithm. The robots share the training data, reducing the

total time required for demonstrations, and exchange the genetic encoding from the

best solutions they've discovered. In these experiments robotic performance on a task

after distributing either the genetic encoding for behavior or the demonstrations used

to learn single behaviors are compared to the performance using a non-distributed

LfD model receiving demonstrations used to learn multiple behaviors. Results show

an improvement in performance when distributing training data on single behaviors

greater than the improvement in performance when the sharing genetic information

of robots trained on multiple behaviors. This implies that robots can learn robust

performance of multiple part tasks by learning each of the individual parts of a task

and distributing the training of the robot.