Patrick MacAlpine

Positioning to Win: A Dynamic Role Assignment and Formation Positioning System

This paper presents a dynamic role assignment and formation positioning system used by the 2011 RoboCup 3D simulation league champion UT Austin Villa. This positioning system was a key component in allowing the team to win all 24 games it played at the competition during which the team scored 136 goals and conceded none. The positioning system was designed to allow for decentralized coordination among physically realistic simulated humanoid soccer playing robots in the partially observable, non-deterministic, noisy, dynamic, and limited communication setting of the RoboCup 3D simulation league simulator. Although the positioning system is discussed in the context of the RoboCup 3D simulation environment, it is not domain specific and can readily be employed in other RoboCup leagues as it generalizes well to many realistic and real-world multiagent systems.

UT Austin Villa: RoboCup 2012 3D Simulation League Champion

The UT Austin Villa team, from the University of Texas at Austin, won the RoboCup 3D Simulation League in 2012 having also won the competition the previous year. This paper describes the changes and improvements made to the team between 2011 and 2012 that allowed it to repeat as champions.

The RoboCup 2013 Drop-In Player Challenges: Experiments in Ad Hoc Teamwork

As the prevalence of autonomous agents grows, so does the number of interactions between these agents. Therefore, it is desirable for these agents to be capable of banding together with previously unknown teammates towards a common goal: to collaborate without pre-coordination. While past research on ad hoc teamwork has focused mainly on theoretical treatments and empirical studies in relatively simple domains, the long-term vision has been to enable robots and other autonomous agents to exhibit the sort of flexibility and adaptability on complex tasks that people do, for example when they play games of “pick-up” basketball or soccer. This paper introduces a series of pick-up robot soccer experiments that were carried out in three different leagues at the international RoboCup competition in 2013. In all cases, agents from different labs were put on teams with no pre-coordination. This paper introduces the structure of these experiments, describes the strategies used by UT Austin Villa in each challenge, and analyzes the results. The papers main contribution is the introduction of a new large-scale ad hoc teamwork testbed that can serve as a starting point for future experimental ad hoc teamwork research.

WrightEagle and UT Austin villa: RoboCup 2011 simulation league champions

The RoboCup simulation league is traditionally the league with the largest number of teams participating, both at the international competitions and worldwide. 2011 was no exception, with a total of 39 teams entering the 2D and 3D simulation competitions. This paper presents the champions of the competitions, WrightEagle from the University of Science and Technology of China in the 2D competition, and UT Austin Villa from the University of Texas at Austin in the 3D competition.

UT Austin Villa: RoboCup 2015 3D Simulation League Competition and Technical Challenges Champions

The UT Austin Villa team, from the University of Texas at Austin, won the 2015 RoboCup 3D Simulation League, winning all 19 games that the team played. During the course of the competition the team scored 87 goals and conceded only 1. Additionally the team won the RoboCup 3D Simulation League technical challenge by winning each of a series of three league challenges: drop-in player, kick accuracy, and free challenge. This paper describes the changes and improvements made to the team between 2014 and 2015 that allowed it to win both the main competition and each of the league technical challenges.

Keyframe Sampling, Optimization, and Behavior Integration: Towards Long-Distance Kicking 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 paper 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) embedding the optimized skill in a full behavior. Called KSOBI, this method is fully implemented and tested in the complex RoboCup 3D simulation domain. To the best of our knowledge, the resulting skill kicks the ball farther in this simulator than has been previously documented.

A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer

Hierarchical task decomposition strategies allow robots and agents in general to address complex decision-making tasks. Layered learning is a hierarchical machine learning paradigm where a complex behavior is learned from a series of incrementally trained sub-tasks. This paper describes how layered learning can be applied to design individual behaviors in the context of soccer robotics. Three different layered learning strategies are implemented and analyzed using a ball-dribbling behavior as a case study. Performance indices for evaluating dribbling speed and ball-control are defined and measured. Experimental results validate the usefulness of the implemented layered learning strategies showing a trade-off between performance and learning speed.

UT Austin Villa RoboCup 3D Simulation Base Code Release

This paper presents a base code release by the UT Austin Villa RoboCup 3D simulation team from the University of Texas at Austin. The code release, based off the 2015 UT Austin Villa RoboCup champion agent, but with some features such as high level strategy removed, provides a fully functioning agent and good starting point for new teams to the RoboCup 3D simulation league. Additionally the code release offers a foundational platform for conducting research in multiple areas including robotics, multiagent systems, and machine learning.

Overlapping Layered Learning

Abstract Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layered learning is that higher layers directly depend on the learned lower layers. In its original formulation, lower layers were frozen prior to learning higher layers. This article considers a major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the “seams” where their influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using such overlapping layered learning, learned a total of 19 layered behaviors for a simulated soccer-playing robot, organized both in series and in parallel. To the best of our knowledge this is more than three times the number of layered behaviors in any prior layered learning system. Furthermore, the complete learning process is repeated on four additional robot body types, showcasing its generality as a paradigm for efficient behavior learning. The resulting team won the RoboCup 2014 championship with an undefeated record, scoring 52 goals and conceding none. This article includes a detailed experimental analysis of the teams performance and the overlapping layered learning approach that led to its success.

Prioritized Role Assignment for Marking

This paper presents a system for marking or covering players on an opposing soccer team so as to best prevent them from scoring. A basis for the marking system is the introduction of prioritized role assignment, an extension to SCRAM dynamic role assignment used by the UT Austin Villa RoboCup 3D simulation team for formational positioning. The marking system is designed to allow for decentralized coordination among physically realistic simulated humanoid soccer playing robots in the partially observable, non-deterministic, noisy, dynamic, and limited communication setting of the RoboCup 3D simulation league simulator. Although it is discussed in the context of the RoboCup 3D simulation environment, the marking system is not domain specific and can readily be employed in other RoboCup leagues as prioritized role assignment generalizes well to many realistic and real-world multiagent systems.