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Hand Posture Subspaces for Dexterous Robotic Grasping Hand Posture Subspaces for Dexterous Robotic Grasping

Hand Posture Subspaces for Dexterous Robotic Grasping - PowerPoint Presentation

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Hand Posture Subspaces for Dexterous Robotic Grasping - PPT Presentation

Dipartimento di Ingegneria dellInformazione Università degli Studi di Siena IIT Genova 24 January 2011 A review of Columbia Universitys work Targets Outline ID: 263988

dimensional hand grasp posture hand dimensional posture grasp grasping optimization dexterous synthesis human ciocarlie obtained position robotic allen interactive

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Slide1

Hand Posture Subspaces for Dexterous Robotic Grasping

Dipartimento di Ingegneria dell’Informazione Università degli Studi di Siena IIT- Genova 24 January 2011

A review of Columbia University’s workSlide2

Targets

Outline affinities with Hands.dvi projectUnderline Ciocarlie and Allen’s resultsFind possible suggestions and define a different way for our projectSlide3

Eigengrasps (1/2)

Low-dimensional hand posture subspaces to express coordination patterns between multiple DOFs for robotic handsBased on Santello’s resultsSame meaning of the synergies vectorsDefined on a 20 DOFs human hand model, the concept has been extended to different robotic hands- M.T. Ciocarlie and P.K. Allen, “Hand Posture Subspaces for Dexterous Robotic Grasping,” The International Journal of Robotics Research, vol. 28, Jun. 2009, pp. 851-867. - M.T. Ciocarlie, C. Goldfeder, and P.K. Allen, “Dexterous Grasping via Eigengrasps : A Low-dimensional Approach to a High-complexity Problem,” Proceedings of the Robotics: Science & Systems, 2007. -

C.Goldfeder, M.T. Ciocarlie, and P.K. Allen, “Dimensionality reduction for hand-independent dexterous robotic grasping,” IROS 07,

Citeseer, 2007.Slide4

Eigengrasps (2/2)

Empirical mapping on non-human handsUse similarities with human handsFor Barreth Hand, spread angle DOF mapped into human finger abduction Slide5

Grasp Synthesis through Low-dimensional Posture Optimization (1/5)

Control algorithms operate on eigengrasp directions and they do not need to be customized for low-level operationsAll of the results presented were obtained by treating all hand models identically, without the need for any hand-specific tuning or change in parametersForm closureMaximization of a high-dimensional quality function p hand posture, w wrist position and orientation, d number of hand DOFsSlide6

Grasp Synthesis through Low-dimensional Posture Optimization (2/5)

If d=20 then 26-dimensional optimization domain

Proposed solution

New problem

Only 8 parameters to compute when

b

=2Slide7

Grasp Synthesis through Low-dimensional Posture Optimization (3/5)

- Quality function formulation- Simulated annealing used for optimizationSlide8

Grasp Synthesis through Low-dimensional Posture Optimization (4/5)

Obtained graspsSolution: use this result as pre-grasp position and complete the grasping by closing fingersSlide9

Grasp Synthesis through Low-dimensional Posture Optimization (5/5)

ResultsNumber of form-closed grasps obtained from 20 pre-grasps found in a two-dimensional eigengrasp spaceSlide10

On-line Interactive Dexterous Grasping (1/2)

Remove computation of wrist position through a human operator that move the handQuality Function Formulation using Scaled Contact Wrench SpacesSlide11

On-line Interactive Dexterous Grasping (2/2)

M.T. Ciocarlie and P.K. Allen, “On-Line Interactive Dexterous Grasping” Slide12

Conclusion

Definition of pre-grasp position obtained in synergy subspaces Definition of eigengrasps for different hand models Form closure grasp obtained from pre-grasp position On-line interactive grasping