RoboticsRobotics

Presenter: Michael BowlingPresenter: Michael Bowling

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Vision StatementVision Statement

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

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MotivationMotivationDeveloping industry with high potential impact on nearly all aspects of society“Dull, Dirty, or Dangerous”

Robots are great testbeds for AI researchRobots are great for outreach

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(Photo from AICML School Visit, 2007)

ML has a key role:Current robot systems are brittle, highly engineered

Our world is diverse, unpredictable, unstructuredChallenging problems for ML:

Complete systemData is noisy, limited, costly to gatherSafety of people, surroundings, robot itselfReal-time demands

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Newest ThrustNewest Thrust

Concerted effort began in 2004/2005

Robotics research requires a teamDiverse talents necessarySizable software systemConsiderable engineering effort

AICML gives a distinct advantage2 full-time software developers1 robotics engineer (recent hire)Still looking for a robotics/ML PDF

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Projects and StatusProjects and Status

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)

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AICML Personnel (cumulative)AICML Personnel (cumulative)

Primary PI’s: Bowling, Schuurmans, Sutton, Szepesvari

8 Software developers 1 Robotic engineer6 Grad students

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ResourcesResources

Grants$300K AIF New Faculty GrantPortion of Rich Sutton’s iCORE chair

Robot Platforms16 Sony Aibo ERS-7s 2 Segway RMPs1 ActivMedia Pioneer 3-DX

Shodan Robotics SimulatorDeveloped and used in-houseOngoing beta-testing for release

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Partners/CollaboratorsPartners/Collaborators

Early Partners:Toyota Motor CorporationUofA Prof in Computer Vision

Future Partners:CCUVS: National initiative located in Alberta

Continued discussions with a number of robotics companies

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Early Highlights Early Highlights

Successful geocaching demonstration (Daily Planet segment)

Most efficient gait learning algorithm (Smithsonian demo)

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Projects and StatusProjects and Status

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)

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Gait LearningGait Learning

Technical DetailsTechnical Details

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The ChallengesThe ChallengesBalance 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 surfaceIndividual robot characteristicsBattery level

Ideal for machine learning!

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Machine Learning ChallengesMachine Learning Challenges

Training takes time; causes wear Use data

efficiently

Data is noisy Explicitly

reason about uncertainty

(Photos from CS Summer Camp, 2006)

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ApproachApproach

Gaussian Process OptimizationPrior over functionsCompute posterior given observationsUse to pick next walk parameters

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ResultsResults

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Gaussian ProcessOptimization

Previous Best

Number of Walks Tested

Quality of Walk

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ConclusionsConclusionsMost efficient published gait learning algorithm (IJCAI, 2007)Optimized walk in 2 hours instead of 10!Little expert knowledge requiredNo 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.

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The FutureThe Future

Exploit the built hardware/software team

New projectsOutdoor navigation with pedestriansMobile robot manipulation

RMP + WAM armRobot minigolf (fall ‘07 grad course)

Open-source robot platform (poster #26)Pursue industrial partnerships

ToyotaCCUVS

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QuestionsQuestions??

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)

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