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Hand Posture Subspaces for Dexterous Robotic Grasping

Hand Posture Subspaces for Dexterous Robotic Grasping. A review of Columbia University’s work. Dipartimento di Ingegneria dell’Informazione Università degli Studi di Siena IIT- Genova 24 January 2011. Targets. Outline affinities with Hands.dvi project

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Hand Posture Subspaces for Dexterous Robotic Grasping

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  1. Hand Posture Subspaces for Dexterous Robotic Grasping A review of Columbia University’s work DipartimentodiIngegneriadell’Informazione UniversitàdegliStudidi Siena IIT- Genova 24 January 2011

  2. Targets Outline affinitieswith Hands.dvi project Underline Ciocarlie and Allen’s results Find possible suggestions and define a different way for our project

  3. Eigengrasps (1/2) Low-dimensional hand posture subspaces to express coordination patterns between multiple DOFs for robotic hands Based on Santello’s results Same meaning of the synergies vectors Defined 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.

  4. Eigengrasps (2/2) Empirical mapping on non-human hands Use similarities with human hands For Barreth Hand, spread angle DOF mapped into human finger abduction

  5. 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 operations All of the results presented were obtained by treating all hand models identically, without the need for any hand-specific tuning or change in parameters Form closure Maximization of a high-dimensional quality function p hand posture, w wrist position and orientation, d number of hand DOFs

  6. 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=2

  7. Grasp Synthesis through Low-dimensional Posture Optimization (3/5) - Quality function formulation - Simulated annealing used for optimization

  8. Grasp Synthesis through Low-dimensional Posture Optimization (4/5) Obtained grasps Solution: use this result as pre-grasp position and complete the grasping by closing fingers

  9. Grasp Synthesis through Low-dimensional Posture Optimization (5/5) Results Number of form-closed grasps obtained from 20 pre-grasps found in a two-dimensional eigengrasp space

  10. On-line Interactive Dexterous Grasping (1/2) Remove computation of wrist position through a human operator that move the hand Quality Function Formulation using Scaled Contact Wrench Spaces

  11. On-line Interactive Dexterous Grasping (2/2) M.T. Ciocarlie and P.K. Allen, “On-Line Interactive Dexterous Grasping”

  12. 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

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