Samuel Barrett
Ph.D. Student
Sam is researching ad hoc teamwork and reinforcement learning. His research focuses on how to cooperate with unknown teammates to accomplish a shared task with no precoordination. As part of RoboCup's Standard Platform League, he works on creating actions, teamwork, and intelligent behaviors for robot soccer. He was previously an NDSEG fellow, and he also worked at MIT Lincoln Laboratory in applying machine learning to detect terrorist threats. Outside work, Sam enjoys rock climbing, ultimate frisbee, racquetball, and playing flute.
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Cooperating with Unknown Teammates in Complex Domains: A Robot Soccer Case Study of Ad Hoc Teamwork 2015
Samuel Barrett and Peter Stone, In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 2015.
Communicating with Unknown Teammates 2014
Samuel Barrett, Noa Agmon, Noam Hazon, Sarit Kraus, and Peter Stone, In Proceedings of the Twenty-First European Conference on Artificial Intelligence, August 2014.
Cooperating with Unknown Teammates in Robot Soccer 2014
Samuel Barrett and Peter Stone, In AAMAS Autonomous Robots and Multirobot Systems Workshop (ARMS 2014), May 2014.
Cooperating with Unknown Teammates in Robot Soccer 2014
Samuel Barrett and Peter Stone, In AAAI Workshop on Multiagent Interaction without Prior Coordination (MIPC 2014), July 2014.
Modeling Uncertainty in Leading Ad Hoc Teams 2014
Noa Agmon, Samuel Barrett, and Peter Stone, In Proc. of 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), May 2014.
The RoboCup 2013 Drop-In Player Challenges: A Testbed for Ad Hoc Teamwork 2014
Patrick MacAlpine, Katie Genter, Samuel Barrett, and Peter Stone, In AAMAS Autonomous Robots and Multirobot Systems Workshop (ARMS 2014), May 2014. Accompanying videos at
The RoboCup 2013 Drop-In Player Challenges: Experiments in Ad Hoc Teamwork 2014
Patrick MacAlpine, Katie Genter, Samuel Barrett, and Peter Stone, In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2014. Accompanying videos at
Currently affiliated with Learning Agents