Presenter: Michael Bowling

2 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. robots

3 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 (Photo from AICML School Visit, 2007) 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

4 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 1. Gait Learning (completed; poster #15) 2. Automatic Calibration (ongoing; poster #24) 3. Hybrid Car Optimization (ongoing) 4. Outdoor Navigation, (ongoing; poster #28) 5. Shodan (ongoing; poster #27)

6 Primary PI ’ s: Bowling, Schuurmans, Sutton, Szepesvari 8 Software developers 1 Robotic engineer 6 Grad students

7 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 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 Successful geocaching demonstration (Daily Planet segment) Most efficient gait learning algorithm (Smithsonian demo)

10 1. Gait Learning (completed; poster #15) 2. Automatic Calibration (ongoing; poster #24) 3. Hybrid Car Optimization (ongoing) 4. Outdoor Navigation, (ongoing; poster #28) 5. Shodan (ongoing; poster #27)

Technical Details

12 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 Training takes time; causes wear  Use data efficiently Data is noisy  Explicitly reason about uncertainty (Photos from CS Summer Camp, 2006)

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

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

16 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 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 1. Gait Learning (completed; poster #15) 2. Automatic Calibration (ongoing; poster #24) 3. Hybrid Car Optimization (ongoing) 4. Outdoor Navigation, (ongoing; poster #28) 5. Shodan (ongoing; poster #27)