Fusing robot behaviors for human-level tasks

Abstract

Behavior-based control is one of the most widely used approaches for autonomous robot control. However, in many robot systems, there is often a disconnect between a user's desired task-level behavior and a robot's preprogrammed (innate) capabilities. Typically, the space of robot behavior is limited to sequential performances, switching between the robot's available skills. Such limited expression does not necessarily overlap with the space of desired robot behavior, leaving users unable to express their true desired control policy to the robot To bridge this divide, a new approach is proposed, which integrates state estimation (as a particle filter), learning by demonstration, and behavior-based control into an approach for robot learning. While these methods have typically been used in different contexts, we demonstrate the ability to use state estimation in order to learn a user's intended control policy from demonstration as a linear combination of innate behaviors. Through a specific navigation task, this method demonstrates how the same task-level behavior can be learned with different combinations of innate behaviors.