This dissertation presents a two level architecture for goal-directed robot control. The low
level actions are learned on-line as the robot performs its tasks, thereby reducing the need …

Two layer control systems are common in robot architectures. The lower level is designed to
provide fast, fine grained control while the higher level plans longer term sequences of …

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 …

This paper describes the use of discrete-event Markov decision process models to design
robust agent controllers in complex stochastic domains. Unlike discrete-time models, where …

A major current challenge in reinforcement learning research is to extend methods that work
well on discrete, short-range, low-dimensional problems to continuous, highdiameter, high …

This paper introduces an integration of reinforcement learning and behavior-based control
designed to produce real-time learning in situated agents. The model layers a distributed …

Recently, techniques based on reinforcement learning (RL) have been used to build
systems that learn to perform non-trivial sequential decision tasks. To date, most of this work …

An ability to coordinate the pursuit of multiple, time-varying goals is important to an
intelligent robot. In this chapter we consider the application of reinforcement learning to a …

To navigate in natural environments, a robot must decide the best action to take according to
its current situation and goal, a problem that can be represented as a Markov Decision …

Autonomous robot systems operating in an uncertain environment have to be able to cope
with new situations and task requirements. Important properties of the control architecture of …