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Fingertip Placement Planning for Unknown Object Grasping

Fingertip Placement Planning for Unknown Object Grasping. Natchanon Wongwilai Adviser: Nattee Niparnan , Ph.D. M.Eng . Outline. Introduction How to grasp?, Why failed to grasp?, Goal Related Works Vision-based grasping, Manipulation under uncertainty Our Problem

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Fingertip Placement Planning for Unknown Object Grasping

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  1. Fingertip Placement Planning for Unknown Object Grasping NatchanonWongwilai Adviser: NatteeNiparnan, Ph.D. M.Eng.

  2. Outline • Introduction • How to grasp?, Why failed to grasp?, Goal • Related Works • Vision-based grasping, Manipulation under uncertainty • Our Problem • Challenge, Proposed method • Etc. • Scopes, Work plan

  3. Robotic Grasping !? [http://spectrum.ieee.org/robotics/robotics-software/slideshow-born-bionic/0] Model µ = 0.53 W = 39 g ? ? ? ? ? ?

  4. How to grasp? ?

  5. Unknown object grasping • 2D [Borst el at.,00; Chinellato el at.,05; Calli el at.,11; ...] • 3D [Miller el at.,03; Goldfeder el at.,07; Hubner el at.,08; ...] • 2.5D [Richtsfeld el at.,08; ...] • Others [Saxena el at.,08; ...]

  6. Grasping in Real World • (Video)

  7. Why failed to grasp? “The most common failure mode I've seen is that the closing fingers bump the object so that the fingers don't touch the intended contact points. Then the fingers knock the object completely out of the grasp. I think the causes are localization errors from the perception system and asking the robot to carry out an inherently dynamic task that was planned with static analysis tools.” Jeff Trinkle GRSSP Workshop 2010 “The most common failure mode I've seen is that the closing fingers bump the object so that the fingers don't touch the intended contact points. Then the fingers knock the object completely out of the grasp. I think the causes are localization errors from the perception system and asking the robot to carry out an inherently dynamic task that was planned with static analysis tools.” Jeff Trinkle GRSSP Workshop 2010

  8. Why failed to grasp? • Contact position error • Theory vs. Practical • Cause of error • Sensor • Control • Computation Uncertainty [http://www.cs.columbia.edu/~cmatei/]

  9. Goal • Accuracy of fingertip placement • Planning • Using camera

  10. Sensor Tactile sensor [Bekiroglu el at., 11] Laser range finder Camera

  11. Related works • Vision-based grasping • Stereo camera • Eye-in-hand camera • Manipulation under uncertainty • Independent contact region • Visual servoing • Reactive grasping • Probabilistic model

  12. Vision-based grasping • Stereo vision based grasping • [Popovic et al.,11; Gratal el at., 12]

  13. Vision-based grasping • Eye-in-hand camera • [Walck el at., 10; Lippielloel at., 11; Calliel at., 11]

  14. Eye-in-hand grasping • (Video)

  15. Manipulation under uncertainty • Independent contact region • [Nilwatchararang et al., 08; Roa et al.,09]

  16. Manipulation under uncertainty • Visual servoing • [Gratal el at., 12; Calli el at., 11]

  17. Manipulation under uncertainty • Reactive Grasping • [Teichmann et al.,94; Hsiao et al.,09; Hsiao et al.,10]

  18. Manipulation under uncertainty • Probabilistic model • [Laaksonen et al.,11; Dogar et al.,11; Platt et al.,11]

  19. Our Problem • Propose online planning method for accurate fingertip placement under uncertainty using eye-in-hand camera

  20. Challenge • ACCURACY!!! • Insufficient information • Bearing-only data • Unknown object model and properties • Don’t have any initial information • Close-up view with featureless image • Kinematic constraint • Unreachable position • Object out of view • Uncertainty • Unpredictable noise

  21. Proposed method

  22. How to grasp? (revisited)

  23. Our method

  24. Simultaneous Localization And Mapping (SLAM) • Robot build up a map and localize itself simultaneously while traversing in an unknown environment [Paul Newman, 06]

  25. Localization & Modeling • Robot location Hand(Fingertips) location • Environment map Object model

  26. Probabilistic map http://www.biorobotics.org/projects/tslam/experiments/slam1experiment.html

  27. SLAM algorithm • Probabilistic SLAM [Smith and Cheeseman, 86] • The probability distribution of robot state and landmark locations • The observation model • The motion model

  28. SLAM algorithm • SLAM recursive algorithm • Time-update • Measurement Update

  29. Observation model • Feature detection • Point features, Line features • Feature association • How features associate with landmarks • Feature measurements • Observation model [http://www.sciencedirect.com/science/article/pii/S0377042711002834]

  30. Map representation • How to represent a map (object model) from available features [http://www.deskeng.com/articles/aaayex.htm]

  31. Planning • Exploration • How to explore for object modeling • Strategy • Close-up strategy • Out of view strategy

  32. Evaluation • Fingertips placement evaluation • Using ground truth data • Contact position marking • Modeling evaluation • Using ground truth data from structural environment • Database • Kinect

  33. Scopes • Develop online planning method for accurate fingertip placement using eye-in-hand camera • Not develop algorithm to find grasping points • No clutter in work space • Simple & Textured object

  34. Work Plan • Study the works in the related fields • Develop algorithms • Test the system • Evaluate a result • Prepare and engage in a thesis defense

  35. Thank you

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