Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

1. Designing Robot Competitions That Promote AI Solutions:Lessons Learned Competing and Designing Jeffrey R. Croxell Ross Mead Jerry B. Weinberg

2. Introduction • Robotics competitions are an excellent educational tool at the middle school, high school, and university levels • Gameplay of competition impacts emphasis for robot designs... • Rouse 2001 • DARPA, RoboCup, and AAAI competitions… • emphasize outstanding research issues in AI • can be cost prohibitive • requires significant human labor and team size • Other competitions typically require little use of AI… • open-loop designs • Large gap between these types Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

3. Introduction • Small-scale competitions can still promote AI solutions and closed-loop designs… • smaller physical size • less costly • easier to handle • Help bridge the gap from simpler to more advanced competitions Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

4. Our Design • General purpose • Lynxmotion… • 4WD1 • 5 DoF arm • XBC • Light sensors • Sonar • IR rangefinders • Camera Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

5. IEEE Region 5 Competition • Mini warehouse • Automated sorter • 4 colored cans placed at random locations • Sort into proper room Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

6. 2006 Beyond Botball • Head-to-head • Remove “toxic waste” • Save Billy and Betty Botguy Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

7. Competing Designs • Highly engineered designs • Movement based on little-to-no sensor feedback… • no obstacle avoidance • Grabbed objects from known locations… • no searching • Simple and effective Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

8. Competition Results • IEEE… • finalists recognized dominant strategy • Beyond Botball… • highly engineered design → faster and more effective • Winner’s circles of these competitions consisted primarily of robots with little intelligence… • more intelligent robots may be overkill for these competitions Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

9. Designer’s Perspective • Annual SIUE Robotics Competitions (based on design in Martin 2001) roboti.cs.siue.edu Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

10. Lessons Learned • Given game specifics, teams assume… • known layout of the arena • no mapping necessary • locations of objects and goals • no searching necessary • series of actions to achieve objectives • no localization or motion planning necessary • static arena state • closed-world assumption (Murphy 2000) Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

11. “Real Robots Don’t Drive Straight” “… students are unlikely to develop feedback-based approaches in their designs of mobile robots in contest events.” — Martin 2007 Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

12. Emphasizing AI Solutions • Promote the use of closed-loop designs… • degrees of uncertainty must be included in the rules of the game • Encourage planning and re-planning based on physical interactions… • with the environment • with objects • with other robots • Intelligent decision-making takes time! • not much is offered by current competitions Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

13. Opportunities in Mapping • Eliminate the closed-world assumption… • do not specify all characteristics of the field of play • Environment can be obstructive and interactive… • present robots with interesting situations and opportunities RoboFest www.robofest.net moveable wooden barrier Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

14. Opportunities in Localization • Require robots to traverse much of the arena board… • dead reckoning is adequate for current competitions • error accumulates quickly • Provide a means for determining spatial position and orientation while navigating the world… • physical and visual landmarks Beyond Botball 2006 www.botball.org FireBot Challenge roboti.cs.siue.edu Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

15. Opportunities in Object Recognition • Place game objectives at unspecified locations… • if the locations of these objects are given, there is no need for a search! • encourages the use of sensors to locate and approach game objects RoboSoccer Shootout! roboti.cs.siue.edu Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

16. Opportunities inNavigation and Planning • Unknown locations/order of objectives… • difficult to rely on a fixed navigational strategy • Navigation involves obstacle avoidance • More complex interactions with objects… • planning for object manipulation Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

17. Opportunities in Interactions FireBot Challenge roboti.cs.siue.edu • Include obstacles! • expected or unexpected • traversable or obstructive • game board itself • Allow for or encourage nondestructive interactions between competing robots… • promotes use of sensory feedback to predict, avoid, or handle collisions Beyond Botball 2006 www.botball.org Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

18. Opportunities in Time • Sensing the environment and making decisions based on this feedback is a time-consuming process… • constrains AI-type strategies the most! • Provide enough time for robots to examine surroundings and calculate responses Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

19. An Example • Search-and-rescue… • 10’x10’ arena representing an earthquake-damaged warehouse • “Blueprint” is given… • unknown conditions inside • Robots explore the arena and search for victims… • all wearing red uniforms Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

20. An Example • As the robot traverses the arena, it must sense objects… • avoid obstacles • approach, confirm (signal), and map victims found • Dead reckoning is primary method of localization… • known colored landmarks and tone emitters for recalibration • 35º incline to second floor • 15 minute time period Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

21. Conclusions • Rules must encourage AI solutions • Environment should reward sensory reaction and high-level decision-making • Important elements: • unspecified dimensions • random placement • time constraints • Techniques utilized help provide an introduction to higher-level concepts and challenges Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

22. Arkin, R. 1998. Behavior-Based Robotics. The MIT Press. Kumar, D. and Meeden, L. 1998. “A Robot Laboratory for Teaching Artificial Intelligence” in Proceedings of the Twenty-ninth SIGCSE Technical Symposium on Computer Science Education, D. Joyce, ed. ), Volume 30, Number 1, Pages 341--344, ACM Press, March 1998. Laird, J.E. and van Lent, M. 2001. “Computer Game Tutorial”, Tutorial Program at the Seventeeth International Joint Conference on Artificial Intelligence, Seattle, WA. Martin, F.G. 2001. Robotic Explorations: A Hands-On Introduction to Engineering, Prentice Hall, Upper Saddle River, NJ. Martin, F.G. 2007. “Real Robots Don’t Drive Straight” in Robots and Robot Venues: Resources for AI Education: Papers from the AAAI Spring Symposium, March 2007. Mayer, G., Weinberg, J.B., and Yu, X., 2004. “Teaching Deliberative Navigation Using the LEGO RCX and Standard LEGO Components”, Accessible Hands on Artificial Intelligence and Robotics Education: Working Papers of the 2004 AAAI Spring Symposium Series, March 2004. Miller, D. and Stein, C. 2000. “‘So That’s What Pi is For!’ and Other Educational Epiphanies from Hands on Robotics”, Robots for Kids: Exploring New Technologies for Learning, A. Druin and J. Hendler, (Eds.), Morgan Kaufmann, pp. 220-243. Murphy, R. 2000. An Introduction to AI Robotics. The MIT Press. Rouse, R. 2001. Game Design: Theory and Practice. Wordware Publishing, Inc., Plano, TX. Weinberg, J.B.,  W. White, C. Karacal, G. Engel, & A. Hu, “Multidisciplinary Teamwork in a Robotics Course”, The 36th ACM Technical Symposium on Computer Science Education, February 2005, pp. 446-450. References Designing Robot Competitions That Promote AI Solutions: Lessons Learned Competing and Designing

23. Questions?