This article describes a system, called Robel, for defining a robot controller that learns from experience very robust ways of performing a high-level task such as "navigate to". The designer specifies a collection of skills, represented as hierarchical tasks networks, whose primitives are sensory-motor functions. The skills provide different ways of combining these sensory-motor functions to achieve the desired task. The specified skills are assumed to be complementary and to cover different situations. The relationship between control states, defined through a set of task-dependent features, and the appropriate skills for pursuing the task is learned as a finite observable Markov decision process (MDP). This MDP provides a general policy for the task; it is independent of the environment and characterizes the abilities of the robot for the task. © 2007 Elsevier B.V. All rights reserved.
Morisset, B., & Ghallab, M. (2008). Learning how to combine sensory-motor functions into a robust behavior. Artificial Intelligence, 172(4–5), 392–412. https://doi.org/10.1016/j.artint.2007.07.003