Multilayered Skill Learning and Movement Coordination for Autonomous Robotic Agents

Download

Defense

Abstract

BibTeX

@PhdThesis{THESIS17-MacAlpine,
author = {Patrick MacAlpine},
  title  = {Multilayered Skill Learning and Movement Coordination for Autonomous Robotic Agents},
  school = {The University of Texas at Austin},
  year = {2017},
  address = {Austin, Texas, USA},
  month = {August},
  abstract = {With advances in technology expanding the capabilities of
robots, while at the same time making robots cheaper to manufacture, robots
are rapidly becoming more prevalent in both industrial and domestic settings.
An increase in the number of robots, and the likely subsequent decrease in the
ratio of people currently trained to directly control the robots, engenders a
need for robots to be able to act autonomously. Larger numbers of robots
present together provide new challenges and opportunities for developing
complex autonomous robot behaviors capable of multirobot collaboration and
coordination.
The focus of this thesis is twofold. The first part explores applying machine
learning techniques to teach simulated humanoid robots skills such as how to
move or walk and manipulate objects in their environment. Learning is
performed using reinforcement learning policy search methods, and layered
learning methodologies are employed during the learning process in which
multiple lower level skills are incrementally learned and combined with each
other to develop richer higher level skills. By incrementally learning skills
in layers such that new skills are learned in the presence of previously
learned skills, as opposed to individually in isolation, we ensure that the
learned skills will work well together and can be combined to perform complex
behaviors (e.g. playing soccer). The second part of the thesis centers on
developing algorithms to coordinate the movement and efforts of multiple
robots working together to quickly complete tasks. These algorithms prioritize
minimizing the makespan, or time for all robots to complete a task, while also
attempting to avoid interference and collisions among the robots. An
underlying objective of this research is to develop techniques and
methodologies that allow autonomous robots to robustly interact with their
environment (through skill learning) and with each other (through movement
coordination) in order to perform tasks and accomplish goals asked of them.
The work in this thesis is implemented and evaluated in the RoboCup 3D
simulation soccer domain, and has been a key component of the UT Austin Villa
team winning the RoboCup 3D simulation league world championship six out of
the past seven years.
  }
}