Nick Malone, Aleksandra Faust, Brandon Rohrer, John Wood, Lydia Tapia, "Efficient Motion-based Task Learning," Accepted to Robot Motion Planning Workshop, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, October 2013.

Abstract— Generating motions for robot arms in real-world
complex tasks requires a combination of approaches to cope
with the task structure, environmental noise, and hardware
imperfections. In this paper we present an efficient framework
for adaptive motion task learning on real hardware that consists
of task transfer, probabilistic roadmaps (PRM), and an online
reinforcement learning algorithm. Online refers to the agent
making decisions and then receiving information about that
decision immediately after the decision has been made, instead
of receiving a complete training set. The task transfer jump
starts training on the hardware with knowledge learned in
simulation. To achieve faster trainings speeds we integrate a
PRM with the learning agent. For motion-based task learning,
we use a reinforcement learning algorithm loosely based on
human cognition. We demonstrate the framework by applying
it to two pointing tasks on a 7 degree of freedom Barrett Whole
Arm Manipulator (WAM) robot. The first task has a stationary
target and illustrates the ability of the framework to quickly
adapt and compensate for hardware noise. The second task
goes a step further and introduces a non-stationary target,
demonstrating the framework’s ability to adapt quickly to a
new environment and new task.