1. Natchanon Wongwilai Adviser: Nattee Niparnan, Ph.D. M.Eng. 1

2. 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 2

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

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5. 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;...] 5

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7. “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 7 “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. Contact position error Theory vs. Practical Cause of error Sensor Control Computation Uncertainty 8 [http://www.cs.columbia.edu/~cmatei/]

9. Accuracy of fingertip placement Planning Using camera 9

10. SensorPriceAccuracyData type Tactile sensorExpensiveHighForce array Laser range finderExpensiveHighRange CameraVaryModerateImage Tactile sensor [Bekiroglu el at., 11] Laser range finderCamera 10

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

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

13. Eye-in-hand camera [Walck el at., 10; Lippiello el at., 11; Calli el at., 11] 13

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15. Independent contact region [Nilwatchararang et al., 08; Roa et al.,09] 15

16. Visual servoing [Gratal el at., 12; Calli el at., 11] 16

17. Reactive Grasping [Teichmann et al.,94; Hsiao et al.,09; Hsiao et al.,10] 17

18. Probabilistic model [Laaksonen et al.,11; Dogar et al.,11; Platt et al.,11] 18

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

20. 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 20

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22. ModelingGrasp planningLocalizationGrasping 22

23. Grasping Localization Modeling Grasp planning 23

24. Robot build up a map and localize itself simultaneously while traversing in an unknown environment [Paul Newman, 06] 24

25. Robot locationHand(Fingertips) location Environment mapObject model 25

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

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

28. SLAM recursive algorithm Time-update Measurement Update 28

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

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

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

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

33. 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 33

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

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