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Generalized Grasping and Manipulation

Explore control-based dexterous manipulation, grasp schemas, contact parameterization, and grasp control potentials in robotics. Learn about sliding grasp control, experiment with transport schema, and enhance grasp skills. Understand controller equivalence classes and action schema for effective robotics applications.

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Generalized Grasping and Manipulation

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  1. Generalized Grasping and Manipulation Robert Platt Jr., Andrew Fagg, Roderic Grupen 5/25/2005 Laboratory for Perceptual Robotics University of Massachusetts Amherst

  2. Motivation: Human Grasps from Mark Cutkosky, On Grasp Choice, Grasp Models, and the Design of Hands for Manufacturing Tasks, IEEE TRA Vol 5, No. 3, June 1989

  3. Outline • Grasp control • Control-based dexterous manipulation • Transport and grasp schemas (skills)

  4. Opposition Space from The Grasping Hand, C. MacKenzie, T. Iberall, Springer, 1994

  5. Contact Parameterization of Grasp 1,2,3 f palm, fingertips grasp 1,2,3 f grasp palm, fingertips left, right f grasp left, right left f grasp left, gravity Platt, R., Fagg, A., Grupen R., 2003

  6. f grasp Grasp Control: Grasp artificial potential displaces contacts to descend wrench-closure error function: Coelho, J., Grupen, R., 1997; Platt, R., Fagg, A., Grupen R., 2002

  7. Sliding Grasp Control Video f f f Sliding Grasp Controller: g k f

  8. f f f f f m k g Four Control Primitives grasp control collision free motion kinematic conditioning force control

  9. Combining Control Primitives Carry: f f f f m k f g Grasp: f f f g k f Platt, R., Fagg, A., Grupen R., 2004

  10. Controller Equivalence Classes: Common Suffix common suffix f f f f m k f g f f f f k k f g f f f f g k f g Platt, R., Fagg, A., Grupen R., 2004

  11. Experiment: Transport Schema

  12. Schematic Transport Skill

  13. Schematic Grasp Skills

  14. Definition of Schema The Action Schema defines: • “Abstract” states and actions • A deterministic policy through the abstract space to the schema goal • A one-to-many mapping from the abstract policy onto instantiations in the underlying system. Subject of learning: • What instantiations are appropriate in what execution contexts?

  15. Localize-Reach-Grasp P(transition | blob parameters)

  16. Reach Primitives Position Orientation

  17. Pilot Results

  18. Pilot Results: Learning Curves (results averaged over three experiments.)

  19. Pilot Results: Frictional Surface

  20. Pilot Results: Bagging Groceries

  21. Pilot Results: Bagging Groceries

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