2011 NSF REU Research Proposal This research is supported by NSF Grant No. CNS 1005212. Opinions, findings, conclusions, or recommendations expressed in.

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2011 NSF REU Research Proposal This research is supported by NSF Grant No. CNS Opinions, findings, conclusions, or recommendations expressed in this presentation are those of the author(s) and do not necessarily reflect the views of NSF.

2011 NSF REU Research Proposal This research is supported by NSF Grant No. CNS Opinions, findings, conclusions, or recommendations expressed in this presentation are those of the author(s) and do not necessarily reflect the views of NSF.

Nao Robot Soccer David Kari, Jesse Kawell Dr. Mohan Sridharan NSF REU 2011

UT Austin Villa Naos across Texas

The Goal ”The stated goal of the Robocup initiative is to develop a team of robots that is better than the best human soccer team by the year 2050.” -Peter Stone et al.

RoboCup is Significant RoboCup provides... An excellent testbed for developing robot software A fun, visual presentation of robotics Multi-agent interaction Human-robot interaction

Related Work The Essence of Soccer, Can Robots Play Too?  Peter Stone, Michael Quinlan, and Todd Hester TT-UT Austin Villa 2009: Naos Across Texas  Todd Hester, Michael Quinlan, Peter Stone, and Mohan Sridharan Half Field O ﬀ ense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study  Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone

Statement of Objectives Multiagent Collaboration on the Nao Platform  Intelligent passing/scoring  Aggressive off-ball offense →high possession %  On-ball pass/shoot balance Project Goal  Score as quickly and efficiently as possible Intermediate Goal  Implement basic plays for offense

Triangle Play

Simulation Strategies are Successful Half Field O ﬀ ense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study -Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone

Project Prerequisites Accurate and timely passing Accurate self-localization Localization of enemies

Current Status 1) Downloaded code, simulator, and compiler on individual PC's 2) Connected to robots R2, D2, C3, and PO 3) Tuned walking parameters to increase speed of walk (min freq=15) 4) Created data logs based on the visual data returned from the robot

Color Maps: Do you see what I see?

Challenges Hardware Issues  R2 fully functioning  D2 ceased normal functioning  C3 just returned!  PO arrived last week from upgrades Robots intermittently lose functionality Understanding scope of software

Anticipated Research Outcomes Robots learn how to collaborate more effectively Given the current set of hardware specifications and constraints the software maximizes team performance

Current Progress

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Related Work The Essence of Soccer, Can Robots Play Too?  Peter Stone, Michael Quinlan, and Todd Hester TT-UT Austin Villa 2009: Naos Across Texas  Todd Hester, Michael Quinlan, Peter Stone, and Mohan Sridharan Half Field O ﬀ ense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study  Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone