A Failure-Tolerant Approach for Autonomous Mobile Manipulation in RoboCup@Work

In this paper we summarize how the LUHbots team was able to win the 2015 RoboCup@Work league. We introduce various failure handling concepts, which lead to the robustness necessary to outperform all the other teams. The proposed concepts are based on failure prevention and failure handling

Meeskonna rUNSWift s ¨usteemi p˜ohjal k¨aitumisloogika arendamine 2015 RoboCup v˜oistluse jaoks

The RoboCup Standard Platform League has two teams, each consisting of five robots play football against each other in a semi-controlled setting. The robots used have the same hardware and modifications are not allowed. The purpose of this thesis was to find a method to improve the overall performance displayed during 2014 RoboCup and implement the method(s). During the course of the project, a new codebase, developed by team rUNSWift, was evaluated, tested and then adopted as it offered improvements compared to the Austin Villa codebase used in 2014. As the codebase offered only basic core functionality, a behaviour module needed to be implemented to offer both low- and high-level behaviours. The behaviours developed provide low-level functionality for movement, ball alignment and targeting and high-level functionality for basic soccer gameplay according to RoboCup 2015 rules. The individual strategy mimics the system used in 2014 with the main difference being the ability to recognize our teammates and then use that information to avoid collisions while trying to hit a ball that is in the common playing area of the two robots. The kick and walk performance appear more stable, as they are both dynamically generated using rUNSWift’s motion system. The walk is also offers greater configurability and needs careful calibration for tuning the input parameters

Hypernetworks Analysis of RoboCup Interactions

Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defender’s Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defender’s Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defender’s dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail

Tinkers: Robots, Makers, and the Changing Face of 21st Century DIY

Project URL: www.tinkers.lindaggorman.com My capstone project takes the form of a website, accessible at the URL above. The site features nine different journalistic pieces reported and produced over the course of the past year, all centered on the themes of robotics and cutting-edge DIY communities. I worked in several different media, from traditional print to audio to infographics. Though this project officially falls under the category of magazine journalism, my capstone also had a heavy technical component associated with creating the website and writing scripts to collect and visualize data. In terms of the content, I reported and wrote about a variety of inventors and creator communities, with a particular focus on robotics. During the course of my research I visited labs and spaces in Japan, Syracuse, Philadelphia, and my hometown of Wilmington, Delaware