semantic robot vision challenge: current state and future directions
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Semantic Robot Vision Challenge: Current State and Future Directions. Scott Helmer, David Meger, Pooja Viswanathan, Sancho McCann, Matthew Dockrey, Pooyan Fazli, Tristram Southey, Marius Muja, Michael Joya, Jim Little, David Lowe, Alan Mackworth. What is the point of robotics research?. - PowerPoint PPT PresentationTRANSCRIPT
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Scott Helmer, David Meger, Pooja Viswanathan, Sancho McCann, Matthew Dockrey, Pooyan Fazli, Tristram Southey,
Marius Muja, Michael Joya, Jim Little, David Lowe, Alan Mackworth
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What is the point of robotics research?To do what humans cannot do:
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What is the point of robotics research?To do tasks that humans prefer not to do:
From WALL-E
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Current State in Home RoboticsOften split in a myriad of subtasks:
navigation, recognition, scene understanding, manipulation, reasoning, etc.
Boundaries and interfaces often ignored and are problematic
Systems engineering is challenging ut generally not publishable
Integrated systems are rare: eg. Stanford’s STAIR, etc.
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Embodied VisionActively “seeing” for some taskHow images are acquired are not considered
traditionally in computer vision, encouraging unrealistic assumptions
Eg. Benchmark datasets in object recognitionnot representative of actual situationslearning algorithms rely on simplificationshard to evaluate whether systems work outside
lab
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What is SRVC?Photo scavenger hunt, where training data is
acquired from internet
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UBC’s ExperienceCurious George (2007, 2008, …)
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UBC and Collaboration
Integrated our labNew research
directionsPlatform on which
to test ideasProvides quick way
to introduce new students
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Designing a winner …Good design choices:
Eye level camera on PTUPeripheral / foveal system with high res. camera
Good Algorithms:SLAM navigationSaliency and visual coverage SIFT based recognitionCategory recognition
After initial phase, can now focus more on research
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What does the SRVC do well?Compelling taskVisibility
AAAI 2007, Vancouver, CanadaCVPR 2008, Anchorage, USAISVC 2009, Las Vegas, USA
Responsive to entrantsEncourages open sourceEvolvesInteresting for audience
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Future Directions for SRVCAttract more competitors
more synthesisgreater exposuremore exciting
Improve research outcomesResearch competitions should advance
research rather than simply display current technology
Should reflect successful research, not engineering that doesn’t transfer
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Attracting CompetitorsCurrently:
2 leagues, Software league and Robot leagueSoftware league is too similar to competitions
like PASCAL VOCRobot league poses challenges due to shipping,
unknown environment, etc.
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Software LeagueOffer more sensory modalities
Stereo vision, high res images, videoOffer mapping info, camera poseLarger test sets for more statistical validityImprove research outcomes (later)
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Robot LeagueProvide more detailed specifications for
contest environmentProvide standardized robot platform and
architecture (like ROS)+ Avoids per team risk of shipping/unknowns+ Provides more opportunities for code sharing- Also involves numerous challenges
Focus on more interesting challenges like viewpoint planning
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Improving Research Outcomes
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Improving Research OutcomesImprove realism – more clutter, occlusions, no
white tablecloths etc.Make context relevantAllow access to pre-built datasets and priors
Web data is generally not suited for 3D recognition
Forefront of vision research requires richer datasets
Greater variety of objects and situationsPoints for
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ConclusionCompetitions can provide an evolving setting
in which to evaluate current technologiesSRVC frames a challenging problem for
embodied vision, which is difficult to evaluate using benchmarks
Numerous changes can be made to attract more competitors and improve research outcomes