1. The Planning & Control of Robot Dexterous Manipulation Li Han, Zexiang Li, Jeff Trinkle, Zhiqiang Qin, Shilong Jiang Dept. of Computer Science Texas A&M University Dept. of Electrical and Electronic Engineering Hong Kong Univ. of Science and Technology Rodin

2. Dexterous Manipulation • Tasks: a robotic hand • grasps an object, and • moves the object from a start configuration to a goal configuration. • Assumptions • Quasi-Static Systems • Rigid Body Motions • preserve distances and orientations • Known System and Environment Parameters

3. Dexterous Manipulation Systems Japan Katharina (Germany) SAMM (O. Khatib, USA)

4. Applications Digital Actor (J.-C. Latombe) Fixture (K. Goldberg) AerCam (NASA) Cellular Man. (Sci. American)

5. Overview • Problem Statement • Force and Motion Feasibility Issues • Manipulation Planning and Control • Experimental Result • Summary HKUST Hand (Z. Li)

6. goal start Dexterous Manipulation • Feasible States • Closure: Variety or Manifold • Feasible Velocities: Tangent Vectors • Feasible Forces: Co-Tangent Vectors • Collision-Free

7. goal start Dexterous Manipulation • Manipulation Planner • Manipulation Controller • Feasible States • Grasp Statics: Force • Manipulation Kinematics: Motion

8. Grasp Statics and Friction Cones Linear Matrix Inequality (LMI)

9. Numerical Results • Convex Programming Involving LMIs (S. Boyd’s Convex Programming Group at Stanford) • Feasibility and Optimization: < 7.82ms (HP/Convex)

10. Manipulation Kinematics • Plan an object trajectory • Use generalized inverse method to find a “best”possible joint trajectory • Infeasible Object Trajectory? • Contact Motion? Manipulation Kinematics: Grasp Kinematics

11. Unreliable Manipulation Plan

12. Modular Control System Architecture

13. Experimental System & Result • Manipulation Objectives • Move the object • Improve the grasp

14. Future Work • Large Scale Object Manipulation in a Crowded Environment • Regrasping and Dexterous Manipulation Planning • Dynamic Constraints • Uncertainty and Robustness • Applications …

15. Conclusion • Grasp Statics • Linear Matrix Inequalities for Nonlinear Friction Cones • Convex Programming • Manipulation Kinematics • Tangent Space (Feasibility Constraints) • Inclusion of all kinematic variables • A Modular Control System Architecture • Manipulation Planning • “Local” Motion in a Clear Environment