<div dir="ltr">Hi everyone,<div><br></div><div>I would also like to share my thesis dissertation with you: the research was largely inspired by my experiences at RoboCup. I participated in RoboCup from 2006-2012, in the Four-Legged League (4LL) and Standard Platform League (SPL). I led the CMurfs SPL team in 2009-2012, and I was primarily focused on the attacker/supporter behaviors and multi-robot coordination within the team. Something that struck me was how we would pick specific robots for specific roles, e.g., "Robot2 should be the attacker since its kicks are the most accurate, and Robot1 should be the goalkeeper since it doesn't walk very quickly."</div>
<div><br></div><div>From there, I formed my thesis topic: Representation, Planning and Learning of Dynamic Ad Hoc Robot Teams. The key idea is that the performance of a robot team is not just the sum of their individual capabilities --- there is <i>synergy</i> among the team members. Using a RoboCup example, the performance of a team is affected by how good the attacker, supporter and goalkeeper robots are at their roles, and more importantly, how well they work together as a team (e.g., how quickly another robot on the team gains possession of the ball if the attacker falls down). My thesis contributes the Synergy Graph model that captures the robots' capabilities and their synergy, and algorithms that learn the model through observations, and selects the best team for the task.</div>
<div><br></div><div>My thesis is applicable to robot teams in general and considers ad hoc teams, i.e., teams of robots that have not collaborated in the past, so their capabilities and synergy are initially unknown. I believe many ad hoc team scenarios exist, since different institutions develop their robots (such as in RoboCup), and a complex task will require combining robots from different sources together in a team (e.g., the drop-in player competition in SPL), so a key question is how to learn their capabilities and synergy, and then form the team for the task.<br>
</div><div><br></div><div><div>The thesis is available for download at: <a href="http://www.somchaya.org/thesis.pdf" target="_blank">http://www.somchaya.org/thesis.pdf</a> I have included my thesis abstract at the bottom of the email for those that are interested.</div>
<div><br></div></div><div>Regards,<br></div><div>Somchaya</div><div><br></div><div><br></div><div><u>Representation, Planning and Learning of Dynamic Ad Hoc Robot Teams</u><br></div><div><br></div><div>Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known.<br>
</div><div><div><br></div><div>Is team performance just the sum of single-robot capabilities? This thesis is motivated by instances where agents perform differently depending on their teammates, i.e., there is synergy in the team. For example, in human sports teams, a well-trained team performs better than an all-stars team composed of top players from around the world. This thesis introduces a novel model of team synergy --- the Synergy Graph model --- where the performance of a team depends on each robot's individual capabilities and a task-based relationship among them.</div>
<div><br></div><div>Robots are capable of learning to collaborate and improving team performance over time, and this thesis explores how such robots are represented in the Synergy Graph Model. This thesis contributes a novel algorithm that allocates training instances for the robots to improve, so as to form an effective multi-robot team.</div>
<div><br></div><div>The goal of team formation is the optimal selection of a subset of robots to perform the task, and this thesis contributes team formation algorithms that use a Synergy Graph to form an effective multi-robot team with high performance. In particular, the performance of a team is modeled with a Normal distribution to represent the nondeterminism of the robots' actions in a dynamic world, and this thesis introduces the concept of a delta-optimal team that trades off risk versus reward. Further, robots may fail from time to time, and this thesis considers the formation of a robust multi-robot team that attains high performance even if failures occur.</div>
<div><br></div><div>This thesis considers ad hoc teams, where the robots of the team have not collaborated together, and so their capabilities and synergy are initially unknown. This thesis contributes a novel learning algorithm that uses observations of team performance to learn a Synergy Graph that models the capabilities and synergy of the team. Further, new robots may become available, and this thesis introduces an algorithm that iteratively updates a Synergy Graph with new robots.</div>
<div><br></div><div>This thesis validates the Synergy Graph model in extensive simulations and on real robots, such as the NAO humanoid robots, CreBots, and Lego Mindstorms NXTs. These robots vary in terms of their locomotion type, sensor capabilities, and processing power, and show that the Synergy Graph model is general and applicable to a wide range of robots. In the empirical evaluations, this thesis demonstrates the effectiveness of the Synergy Graph representation, planning, and learning in a rich spectrum of ad hoc team formation scenarios. </div>
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