Robotics

1. Presenter: Michael Bowling Robotics

2. Vision Statement robots Helping the world understand data and make informed decisions • Potential beneficiaries: • Growing robotics and UVS sector, • Diverse industries (incl. mining, farming, service), • Society as a whole.

3. Motivation • Developing industry with high potential impact on nearly all aspects of society • “Dull, Dirty, or Dangerous” • Robots are great testbeds for AI research • Robots are great for outreach • ML has a key role: • Current robot systems are brittle, highly engineered • Our world is diverse, unpredictable, unstructured • Challenging problems for ML: • Complete system • Data is noisy, limited, costly to gather • Safety of people, surroundings, robot itself • Real-time demands (Photo from AICML School Visit, 2007)

4. Newest Thrust • Concerted effort began in 2004/2005 • Robotics research requires a team • Diverse talents necessary • Sizable software system • Considerable engineering effort • AICML gives a distinct advantage • 2 full-time software developers • 1 robotics engineer (recent hire) • Still looking for a robotics/ML PDF

5. Projects and Status • Gait Learning (completed; poster #15) • Automatic Calibration (ongoing; poster #24) • Hybrid Car Optimization (ongoing) • Outdoor Navigation, (ongoing; poster #28) • Shodan (ongoing; poster #27)

6. AICML Personnel (cumulative) • Primary PI’s: Bowling, Schuurmans, Sutton, Szepesvari • 8 Software developers • 1 Robotic engineer • 6 Grad students

7. Resources • Grants • $300K AIF New Faculty Grant • Portion of Rich Sutton’s iCORE chair • Robot Platforms • 16 Sony Aibo ERS-7s • 2 Segway RMPs • 1 ActivMedia Pioneer 3-DX • Shodan Robotics Simulator • Developed and used in-house • Ongoing beta-testing for release

8. Partners/Collaborators • Early Partners: • Toyota Motor Corporation • UofA Prof in Computer Vision • Future Partners: • CCUVS: National initiative located in Alberta • Continued discussions with a number of robotics companies

9. Early Highlights • Successful geocaching demonstration (Daily Planet segment) • Most efficient gait learning algorithm (Smithsonian demo)

10. Projects and Status • Gait Learning (completed; poster #15) • Automatic Calibration (ongoing; poster #24) • Hybrid Car Optimization (ongoing) • Outdoor Navigation, (ongoing; poster #28) • Shodan (ongoing; poster #27)

11. Technical Details Gait Learning

12. The Challenges • Balance and locomotion is the key problem for legged robots • Gait optimization is hard: • Open loop gait may require >50 parameters • Effect of parameters involve complex interactions • Effective gaits depend upon: • Walking surface • Individual robot characteristics • Battery level • Ideal for machine learning!

13. Machine Learning Challenges • Training takes time; causes wear • Use data efficiently • Data is noisy • Explicitly reason about uncertainty (Photos from CS Summer Camp, 2006)

14. Approach • Gaussian Process Optimization • Prior over functions • Compute posterior given observations • Use to pick next walk parameters

15. Results Gaussian Process Optimization Previous Best Quality of Walk Number of Walks Tested

16. Conclusions • Most efficient published gait learning algorithm (IJCAI, 2007) • Optimized walk in 2 hours instead of 10! • Little expert knowledge required • No starting seed or restarts needed • Also successfully applied to finding parameters in stereo vision and NLP • Exhibited in “Alberta at the Smithsonian” in Washington, D.C., Summer 2006.

17. The Future • Exploit the built hardware/software team • New projects • Outdoor navigation with pedestrians • Mobile robot manipulation • RMP + WAM arm • Robot minigolf (fall ‘07 grad course) • Open-source robot platform (poster #26) • Pursue industrial partnerships • Toyota • CCUVS

18. Questions? • Gait Learning (completed; poster #15) • Automatic Calibration (ongoing; poster #24) • Hybrid Car Optimization (ongoing) • Outdoor Navigation, (ongoing; poster #28) • Shodan (ongoing; poster #27)