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The RoboCup 2013 Drop-In Player Challenges: Experiments in Ad Hoc Teamwork Patrick MacAlpine, Katie Genter, Samuel Barrett, and Peter Stone Department of Computer Science, The University of Texas at Austin September 15, 2014 Patrick MacAlpine

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

Patrick MacAlpine, Katie Genter, Samuel Barrett, and Peter Stone

Department of Computer Science, The University of Texas at Austin

September 15, 2014

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue

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Example Ad Hoc Teamwork Scenario: Earthquake Response & Rescue Pre-coordination may not be possible Agents should be robust to various teammates Agents need to adapt quickly!

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Ad Hoc Teamwork Not in control of all agents on team Unknown teammates Shared goals No pre-coordination Example in humans: Pick up soccer

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Ad Hoc Teamwork Not in control of all agents on team Unknown teammates Shared goals No pre-coordination Example in humans: Pick up soccer Research Question: How can an agent cooperate with unknown teammates?

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RoboCup Drop-In Player Challenges RoboCup is an international robotics competition where autonomous robots play soccer Games between teams consisting of different randomly chosen players from participants in the competition No pre-coordination between teammates, teammates/opponents unknown before start of a game Teams provided standard communication protocol for use during games Testbed for ad hoc teamwork Challenge held across three leagues at the 2013 RoboCup competition

◮ Standard Platform League (SPL) ◮ 2D Simulation League ◮ 3D Simulation League Patrick MacAlpine (2014) 4

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Standard Platform League (SPL) Use Nao robots Teams of 5 vs 5 autonomous robots play soccer Robots can communicate over wifi Click to start

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2D Simulation League Teams of 7 vs 7 autonomous agents play soccer Agents use primitives of "dash", "kick", and "turn" to interact with environment Agents receives noisy visual information about environment Agents can communicate over limited bandwidth channel Click to start

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3D Simulation League Teams of 10 vs 10 autonomous agents play soccer Agents modeled after Aldebaran Nao robot Realistic physics using Open Dynamics Engine (ODE) Agents receives noisy visual information about environment Agents can communicate over limited bandwidth channel Click to start

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Topics Covered in Paper How to create a testbed for ad hoc teamwork How to select/assign agents to different teams How to measure/evaluate/score performance of individual agents in an ad hoc teamwork setting Strategies for ad hoc teamwork

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More Information More information and vidoes at: http://tinyurl.com/iros14dropin Email: patmac@cs.utexas.edu

This work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by grants from the National Science Foundation (CNS-1330072, CNS- 1305287) and ONR (21C184-01).

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