Multilayered skill learning and movement coordination for autonomous robotic agents

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.Computer Science

Cognitive robotics in a soccer game domain: a proposal for the e-league competition

In this work, we will discuss the design of a team of robots to play soccer at RoboCup E-League. This task is being carried out in the Cognitive Robotics group of the Laboratory of Research and Development in Artificial Intellgence (LIDIA), Department of Computer Science and Engineering, Universidad Nacional del Sur. The RoboCup competition provides a great opportunity to develop a multi-agent system in which we can test and apply new ideas and results. In the following sections, we will briefly describe the league in which we will participate, and our proposal for the implementation of a team.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

Cognitive robotics in a soccer game domain: a proposal for the e-league competition

In this work, we will discuss the design of a team of robots to play soccer at RoboCup E-League. This task is being carried out in the Cognitive Robotics group of the Laboratory of Research and Development in Artificial Intellgence (LIDIA), Department of Computer Science and Engineering, Universidad Nacional del Sur. The RoboCup competition provides a great opportunity to develop a multi-agent system in which we can test and apply new ideas and results. In the following sections, we will briefly describe the league in which we will participate, and our proposal for the implementation of a team.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

Designing an agent system for controlling a robotic soccer team

Robotic soccer is a way of putting different developments in intelligent agents into practice, including not only problems such as multi-agent planning and coordination, but also physical problems related to vision and communication subsystems. Because these problems cannot be all taken into account beforehand, the system must be designed to be robust enough to recover from any eventualities. In this work, we present the design used as the basis for an agents system implemented for the control of a team of robots for the E-League competition in RoboCup 2004. The implementation of the system was carried out following a layered design, with the objective of having a set of Service Layers, each of which is associated with a different level of abstraction. This layered design allows to construct a functional system with basic services that can be tested and refined progressively. The layers that are proposed as a basis for the arquitecture of a robotic soccer team offer a modular design, allowing the possibility of reuse in other robotic soccer leagues. Finally, the agents are implemented using the prolog language; the three uppermost layers in the hierarchy offer interfaces designed explicitly for this language.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Keyframe Sampling, Optimization, and Behavior Integration: A New Longest Kick in the RoboCup 3D Simulation League

Even with improvements in machine learning enabling robots to quickly optimize and perfect their skills, developing a seed skill from which to begin an optimization remains a necessary challenge for large action spaces. This thesis proposes a method for creating and using such a seed by i) observing the effects of the actions of another robot, ii) further optimizing the skill starting from this seed, and iii) em- bedding the optimized skill in a full behavior. Called KSOBI, this method is fully implemented and tested in the complex RoboCup 3D simulation domain. The main result is a kick that, to the best of our knowledge, kicks the ball farther in this simulator than has been previously documented.Computer Science

RoCKIn: Impact on Future Markets for Robotics

The goal of the project “Robot Competitions Kick Innovation in Cognitive Systems and Robotics” (RoCKIn), funded by the European Commission under its 7th Framework Program, has been to speed up the progress toward smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europe’s societal and industrial needs: domestic service robots (“RoCKIn@Home”) and innovative robot applications in industry (“RoCKIn@Work”). The history and reasoning behind the chosen task and functionality benchmarks in RoCKIn are explained by providing an insight from the International Federation of Robotics and an analysis on RoCKIn’s impact on the industrial robot market domain is carried out. To paint a broad picture, RoCKIn is compared to other robot competitions and similarities, differences and challenges those competitions share are pointed out. Some industrial robot market requirements and the way RoCKIn addressed them are explained. Strength and weaknesses of the project in regard to their market impact are emphasized and it is shown how these were continued and addressed by RoCKIn’s successor European Robotics League (ERL)

A layered architecture using schematic plans for controlling mobile robots

Robotic soccer is a way of putting different developments in intelligent agents into practice, including not only problems such as multi-agent planning and coordination, but also physical problems related to vision and communication subsystems. In this work, we present the design used as the basis for a multi-agent system, implemented for controlling a team of robots, having as main goal to facilitate the testing of new theories developed on reasoning, knowledge representation, planning, agent communication, among others Artificial Intelligence techniques. The implementation of the system was carried out following a three-layer architecture which consists of a reactive layer, an executive layer and a deliberative layer, each of which is associated with a different level of abstraction. This layered design allows to construct a functional system with basic services that can be tested and refined progressively. We will focus our explanation on the executive layer, responsible for sensorial processing and the execution of schematic plans.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

The RoCKIn Project

The goal of the project “Robot Competitions Kick Innovation in Cognitive Systems and Robotics” (RoCKIn), funded by the European Commission under its 7th Framework Program, has been to speed up the progress toward smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europe’s societal and industrial needs: domestic service robots (RoCKIn@Home) and innovative robot applications in industry (RoCKIn@Work). The RoCKIn project has taken an approach to boosting scientific robot competitions in Europe by (i) specifying and designing open domain test beds for competitions targeting the two challenges; (ii) developing methods for scoring and benchmarking that allow to assess both particular subsystems as well as the integrated system; and (iii) organizing camps to build up a community of new teams, interested to participate in robot competitions. A significant number of dissemination activities on the relevance of robot competitions were carried out to promote research and education in robotics, as to researchers and lay citizens. The lessons learned during RoCKIn paved the way for a step forward in the organization and research impact of robot competitions, contributing for Europe to become a world leader in robotics research, education, and technology transfer

Rational decision making in autonomous agents

Making rational decisions is one of the key elements in the design of autonomous agents with successful behavior. Even though there have been many proposals for the support of decision making, most of them can be described either as descriptive or prescriptive. The main goal of our work is to establish the relationship between two of these models, namely bdi and mdps, in order to gain further understanding of how decisions in one model are viewed from the point of view of the other. This goal is important for the development of agent design strategies that unite the best of both worlds.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI