Learning to optimize mobile robot navigation based on HTN plans

Abstract

High-level symbolic representations of actions to control the working of autonomous robots are used in all hybrid (reactive and deliberative) robot control architectures. Abstract action representations serve several purposes, such as structuring the control code, optimizing the robot performance, and providing a basis for reasoning about future robot action. The paper presents results about re-designing the RHINO navigation system by introducing an HTN plan layer. Besides yielding a more structured robot control software, this layer is used as a basis for optimizing the navigation performance by plan transformations. We show how a robot can learn to select plan transformations based on projections of its intended behavior. Our experimental evaluation shows that the overall robot navigation performance is increased by almost 42 % when using learned projective models to select plan transformations.