Implementation of Artificial Physics Using AIBO Robots

1. Ankur Desai December 7, 2006 An Implementation of Artificial Physics Using AIBO Robots and the Pyro Programming Environment

2. 4555 Overlook Ave., SW Washington, DC 20375 Naval Research Laboratories Artificial Intelligence Center

3. Mitchell A. Potter, Ph.D. • Principal Investigator • Evolutionary Robotics • Coevolutionary Models • Representation Issues • Continuous and Embedded Learning

4. Adaptive Systems Team • Shared lab space • Variety of robotic equipment • No wireless communications • Upcoming anniversary demonstration

5. Rationale • Divide tasks between multiple robots • Based on natural behaviors • Unified platforms

6. Purpose • Determine whether AIBO is an effective platform for artificial physics • Create Python module to control the AIBO robots

7. Artificial Physics • Developed by Spears and Gordon in 1999 • Each robot treated as a molecule • Gravitational forces simulated

8. Artificial Physics • Grid formation Resource protection

9. Sony AIBO

10. Python Robotics • Interpreted language • Platform-blind • High-level control

11. Testing Design • Straight line accuracy • Turning accuracy • Correct functioning of simulation • No testing necessary

12. Materials • Software • SWIG • Tekkotsu • Pyro • Seven AIBO robots

13. Procedures – Python module • Build C library object files • Create SWIG wrapper • Compile wrapper into dynamic library

14. Procedures – Odometry • Setup • Place AIBO in empty room • Connect to host computer • Send command • Walk 10 meters • Turn 360° • Measure actual motion

15. Straight Line Results

16. Turning Results

17. Conclusion • Python module successful • AIBO is not a suitable platform • Alternate localization techniques • Use of different robotic models

18. Reflections • Overall positive experience • Delayed security clearance • Limited wireless access • Difficult commute

19. Acknowledgments • I would like to thank the Adaptive Systems team at Naval Research Laboratories Artificial Intelligence Center, especially Mitchell Potter and R. Paul Wiegand, for their guidance and support throughout this project.

20. Literature Cited • Blank, D., Meeden, L., & Kumar, D. (2003). Python robotics: An environment for exploring robotics beyond LEGOs. SIGSCE ’03, 35, 317-3121. • Ikemoto, Y., Hasegawa, Y., Fukuda, T., & Matsuda, K. (2005). Gradual spatial pattern formation of homogeneous robot group. Information Sciences, 171, 431-445. • Lee, M. (2003). Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm. Information Sciences, 155, 43-60. • Oliveira, E., Fischer, K., & Stepankova, O. (1999). Multi-agent systems: Which research for which applications. Robotics and Autonomous Systems, 27, 91- 106. • Röfer, T., & Jüngel, M. (2003). Fast and robust edge-based localization in the Sony four-legged robot league. In Polani, D., Browning, B., Bonarini, A., & Yoshida, K. (Eds.), RoboCup 2003: Robot soccer world cup VII (pp. 262-273). Berlin: Springer. • Spears, W. M., & Gordon, D. F. (1999). Using artificial physics to control agents. 1999International Conference on Information Intelligence and Systems, 1999, 281- 288. • Tira-Thompson, E. J., Halelamien, N. S., Wales, J. J., & Touretzky, D. S. (2004). Tekkotsu: Cognitive robotics on the Sony AIBO. Proceedings of the Sixth International Conference on Cognitive Modeling, 6, 390-391.