_grasping robustness to uncertainty in pose 对不确定性的噪声鲁棒性课件

Robot Grasp Planning using Parallel Sampling to Estimate Uncertainty in Pose, Shape, and Mechanics Melissa Goldstein Edward Lee Frank Ong Josh Goldberg Lelai Zhou Ben Kehoe Ken Goldberg UC Berkeley Summary Motivation Related Work Problem Statement Sampling Preliminary Results Future Work Willow Garage PR2 Mobile Base Cameras, Lidar 2 Arms Backdrivable for Safety Low Precision 2 Parallel-Jaw Grippers Holding: Rigid parts Contact Mechanics: Number of contacts Reuleaux, 1876, Somoff, 1900 Mishra, Schwarz, Sharir, 1987, Nguyen, 1988 Markenscoff, Papadimitriou, 1990 Han, Trinkle, Li, 1999 Immobility, 2nd Order Form Closure Rimon, Burdick, 1995, 1998 Ponce, Burdick, Rimon, 1995 Mason, 2001 Holding: Rigid parts +- +- + + - - Summaries of results Bicchi, Kumar, 2000 Mason, 2001 Grasp Regions van der Stappen et al, 2002 Caging Grasps Rimon, Blake, 1999 van der Stappen 2009, Rodriguez, Mason 2010 Parallel-Jaw Grip Points (1999) Related Work: Masons Rule Line of pushing and the edges of the friction cone “vote” to determine which way the object will rotate We want the workpiece to rotate in a direction that will result in alignment with the gripper edge Matthew T. Mason, Mechanics of Robotic Manipulation, MIT Press: Cambridge, MA. 2001. Stable Push Grasps Stable push grasps (SPGs) satisfy the following conditions after the gripper contacts the workpiece and continues pushing: The workpiece purely rotates about the contact point (no slipping) The workpiece rotates toward stability on the gripper face (becomes aligned with the gripper) The second gripper achieves force closure Problem Statement Assume: Part on Worksurface Planar Projections of Part and Gripper Planar, Quasi-static Motion Given: Nominal 2D Polygonal Part Center of mass Shape, Center of Mass Lower Bound on Friction Uncertainty in: Relative Pose Center of mass Shape Problem Statement Perturbations in: Relative Pose Center of mass Shape Push Motion Approach: Stable Push Grasps Position Jaws Make Initial contact with Vertex of Jaw 1 Stable Push with Jaw 1 to Align Edges Close Gripper Contact with Jaw 2 Failure Modes 1. First gripper misses workpiece Failure Modes 2. Gripper contacts outside friction cone (slip) Failure Modes 3. Gripper contacts with too large an angle for the workpiece to maintain sticking after some rotation Failure Modes 4. Gripper contacts on wrong side of friction cone (rotation) Max Contact Angle Center of Mass Inverse Friction Cone 0d2d f: Allowable angle First Gripper Maximal value of f for each point on the edge within the inverse friction cone Angle (in degrees) Note: The C-space plot includes all constraints except ensuring that force closure is attained by the second gripper. f = arctan(m - x/z) x z 0 d2d x Sampling Vertex Positions Sample: Part Vertices and CG within uncertainty zones (500 samples) Sample Relative Pose 50x50 grid Sample Line of Pushing 0.5 degrees Evaluate if SPG Color by % SPG % SPG = 98% % SPG = 74% Rotated Square Number of points Successes (% of 500 Samples) Knight Sampling Edge Normals Sample: Part Edge Normals and CG within uncertainty zones (500 samples) Sample Relative Pose 50x50 grid Sample Line of Pushing 0.5 degrees Evaluate if SPG Color by % SPG Results % SPG = 54 Results % SPG = 10 Results % SPG = 36 Uncertainty May Help? Same uncertainty about all vertices of workpiece Two vertices with greater uncertainty for workpiece Other Related Work Contact sensors Felip and Morales, 2009 Robotic hand with embedded gripper, tactile, pressure, and/or force sensors Sensors estimate quality of the grasp and shape of the object to make live improvements to the grasp 3D environments Nguyen 1987 Sensors create a 3D map of the object and environment Runs an algorithm on the objects geometry to determine a stable grasp Analytical Models Optimize the grasp quality criteria for force closure and local object stability Berenson, Srinivasa, Kuffner 2009 Morales, Sanz, del Pobil, Fagg 2006 BarrettHandTM with pressure sensors Related Work Christopoulos and Schrater, 2007 Spline fitting Directly incorporate uncertainty in shape through spline geometry Test of force closure Related Strategy: Task Space Regions Dmitry Berenson et. al., “Addressing Pose Uncertainty in Manipulation Planning Using Task Space Regions”, The International Conference on Intelligent Robots and Systems, 2009 Task Space Regions TSR analyze the six-dimensional space representing possible goals for a gripper and consider the pose uncertainty in order to avoid potential collisions The rejection sampling with TSR allows to decline if the region is impossible to achieve the task with the uncertainty IKBiRRT find a C-space path to the grasp Dmitry Berenson et. al., “Addressing Pose Uncertainty in Manipulation Planning Using Task Space Regions”, The International Conference on Intelligent Robots and Systems, 2009 Next Steps Allow Slip Use Concavities Consider Torque around CG Adaptive Sampling Parallel-Sampling in Cloud Cloud Computing /wiki/File:Cloud_computing.png Stable Push Grasps Next Steps Potential Methods: After each 50 iterations, eliminate point- angles with less than 20% of the successes of the most successful point Image segmentation: keep areas with an average value that is “high enough,” since relative success of areas shifts with the sampling Related Work: Force Closure Line segment between contact points must lie within the friction cones of the contact points on each edge Van-Duc Nguyen, “Constructing Force- Closure Grasps,” The International Journal of Ro