GraspIt!:%20A%20Versatile%20Simulator%20for%20Robotic%20Grasping

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GraspIt! A Versatile Simulator for Robotic
Grasping

• Andrew T. Miller
• Columbia University

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Outline

• Motivation
• Simulator Overview
• Simulator Front-end
• Grasp Analysis
• Dynamic Simulation
• Performing a Grasping Task
• Future Work

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Simulation for Hand Design

• Mechanical prototypes are costly
• Spend time and money
• Evaluate design in simulation
• Examine range of motion, finger placement,
  kinematics, link geometry
• Ensure it can grasp desired objects
• Change design easily

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Simulation for Grasp Planning

• Integrated grasp analysis
• Grasp quality, weak point, force optimization
• Visualize grasp from all angles
• Perform many grasps quickly
• Faster than using a real arm and hand
• Build a library of saved grasps
• Recall grasp when object is encountered again

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GraspIt A Tool for Grasping Research

• Library of hands and objects
• Intuitive interface allows many grasps to be
  tested quickly
• Visualize grasp wrench space
• Quality measures evaluate grasp

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GraspIt Components
Hand/Object Construction
User Interface
Contact Determination

• read object model
• read link models
• read kinematics
• assemble hand

• view 3D scene
• change hand pose
• auto-grip
• manually move joints

• detect collisions
• adjust contact to object surface
• find contact area
• add friction cones

Front end
Grasp Analysis
Wrench Space Visualization
Object Dynamics
Back end

• compute grasp wrench space
• use metrics on space
• grasp force optimization

• create 3D projections of GWS

• compute object motion

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Hand Construction

• The hand kinematics (in D-H notation) specify the
  transforms between links, and can handle coupled
  joints.
• The 3D link geometries are accurately described
  in CAD model files.
• The format is flexible and can easily model many
  of the available complex articulated hands.

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Hand Configuration File

• Header info and palm description ...
• -----------------------------------f0------------
  ---------------------------
• number of joints
• 3
• number of links
• 3
• Transforms from palm origin to base of the
  finger
• EXAMPLE
• r 90 x (rotation by 90 degrees about x axis)
• rr 3.0368729 y (rotation by 3.0368729 radians
  about y axis)
• t 1.92188 134.5 39.0717 (translation in mm)
• t 25.0 0 -1.0
• r 180 y

• Joint Descriptions (1 joint per line)
• DOFNum theta d a alpha DOFminval DOFmaxval
• (joints are ordered from closest to palm
  outward)
• (linear equations are of the form qkc no
  spaces!)
• (coupled joints must come after joints they are
  coupled to)
• d090 0 50 -90 0 180
• d15 0 70 0 0 144
• d10.33333333340 0 55 0 0 144
• Link Descriptions (1 link per line)
• filename material lastJoint
• (links are ordered from closest to palm outward)
• (lastJoint is the last joint in the chain which
  can affect this link)
• link1 plastic 0
• link2 plastic 1
• link3 plastic 2
• -----------------------------------f1------------
  ---------------------------
• etc...

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Hand Library
Barrett
Rutgers
Robonaut
Parallel Jaw
DLR
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User Interaction

• Inventor 3D viewer
• Rotate hand
• Translate hand
• Auto-grasp
• Manipulate individual DOFs
• Simple and intuitive
• Direct interaction gives sense of hands
  capabilities

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Collision Detection

• PQP system (Larsen et al.)
• Each body is a polygonal soup
• Creates bounding volume hierarchies
• Fast recursive algorithm tests for overlap, or
  provides minimum distance

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Finding Contacts
Search for instant of contact
Determine contact region
Collision detected

• Binary search until link is lt 0.1mm away
• Record all triangle pairs within this distance
• Compute points of overlap between each pair
• Only keep contact points on region boundary
• Mark points with red friction cones

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Grasp Analysis

• Occurs when a contact is formed or broken
• Computes space of forces and torques that can be
  applied by the grasp
• Quality measures numerically evaluate grasp
• Provides a means to evaluate grasps
• Compare grasps of one hand, one object
• Compare grasps of many hands, one object
• Compare grasps of many hands, across a task
  specified object set.

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Wrench Spaces

• In 3-space, a wrench is a 6D vector composed of a
  force and a torque
• The space of wrenches that may need to be applied
  during a task is the task wrench space.
• The space of wrenches that can be applied by a
  grasp is the grasp wrench space.
• A possible quality measure

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Special Types of Grasps

• A force-closure grasp completely restrains the
  object.
• Origin is contained within grasp wrench space.
• A manipulable grasp can impart arbitrary
  velocities on the object without breaking
  contact.

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Friction Cones

• Friction at a contact point allows forces in
  directions other than the contact normal
• COF, m, is determined by the contacting
  materials
• Estimate friction cone as convex sum of a force
  vectors on the boundary assuming a unit normal
  force, .

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Contact Wrenches

• Each force acts at a position on object.
• Compute the corresponding wrench with respect to
  objects center of gravity
• l - torque scale factor,

i contact number j (1-8) around cone boundary
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Grasp Wrench Space

• Objective find total space of wrenches that can
  be applied by a grasp of unit magnitude.
• Grasp vector
• Define with norm
• Sum magnitude of contact normal forces is 1.
• Compute grasp wrench space using qhull

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Two Measures of Quality

• Assume task wrench space is unknown.
• Estimate with wrench space ball - good grasps
  will resist all wrenches equally well.
• Previously proposed measures of quality
• Radius, e, of the largest wrench space ball that
  can fit within the unit grasp wrench space.
• Volume, v, of unit grasp wrench space.

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Grasp Wrench Space Projections

• To visualize the 6D grasp wrench space project it
  to 3-space by fixing three coordinates.
• A useful choice
• See the forces that can be applied without
  applying a net torque (or vice versa).

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Visualizing the Results
Numeric quality Worst case indicator
Wrench Space Projection
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Comparing Grasps
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Comparing Grasps
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Comparing Grasps
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Comparing Grasps
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Grasp Force Optimization

• Given a specific wrench to resist, find the
  optimal force to apply at contact points
• Forces should obey torque limit constraints and
  avoid friction cone boundaries
• Previously done by using linearized friction
  cones and linear programming Kerr Roth 86
• overly conservative

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Grasp Force Optimization

• GraspIt implements LMI technique by Han et al.
  00,
• Does not require friction cone approximation
• Feasible Set
• Minimize
• Determinant Maximization with LMI constraints
• Using hand Jacobian, it can also compute optimal
  joint torque values

Contact forces
Inverse friction cone LMI
Weighting factor
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Grasp Force Optimization
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Simulating Object Dynamics

• Added realism
• Can examine grasp formation process
• Formulate non-penetration and friction cone
  constraints as a Linear Complementarity Problem
  Stewart Trinkle 00
• Lemkes algorithm solves LCP to find contact
  forces that satisfy constraints.

force OR acceleration must be 0
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Simulating Object Dynamics
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Simulating Object Dynamics
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Connecting GraspIt to theReal World
Object Pose Estimation
Frame Grabber
Robot/Hand Control
Grasp Execution
Task Monitoring
Presented at ICRA 01
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Environment Modeling

• Easy to import environmental objects like
  workbench.
• Puma arm with inverse kinematics module also
  supplied.
• Prevents user from planning infeasible grasps.

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Real-Time Vision System

• Supply GraspIt with object pose.
• Needs to track object that is partially occluded
  by the hand.
• Since we have an object model, why not use
  model-based pose estimation and tracking?
• Single camera located 2m from workspace,
  calibrated wrt robot coordinate system.

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Real-Time Pose Estimationand Tracking

• Position estimation based on Dementhon and Davis.
• Tracking based on Araujo and Browns extension of
  Lowe.

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Tracking System Example
Wireframe model Selected points are used to
determine pose
Pose estimated, and model overlaid
Normal flow estimation
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Grasp Execution Experiments
Pose estimated
User plans grasp
Grasp performed
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Grasp Execution Experiments
Pose estimated
User plans grasp
Grasp performed
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Grasp Execution Experiments
Pose estimated
User plans grasp
Grasp performed
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Grasping and Visual Servoing
Once pose has been estimated, grasp may be
planned,
then executed.
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Grasping and Visual Servoing
Destination planned in GraspIt.
Goal position is projected into image.
Image based servoing drives robot to goal.
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Planning a Grasping Task in GraspIt
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Planning a Grasping Task in GraspIt
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Planning a Grasping Task in GraspIt
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Contributions

• Grasping simulator
• Flexible method to specify hand construction
• Interactive interface
• Contact determination system
• Inclusion of material properties
• Integrated grasp analysis theory into simulation
  system
• GWS, quality measures, GFO
• Visualization techniques
• Object Dynamics First implementation of S-T
  algorithm with large scale and interactive bodies
• Integration of simulation system into grasping
  task
• A useful tool for research in grasping and hand
  design
• Currently being used by a number of research
  groups for design and synthesis NASA, Sandia,
  Rutgers, U of MD

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Future Work

• Evaluate manipulable grasps
• Provide method of specifying task wrench space
• Human hand model
• Full dynamic simulation
• Automatic grasp selection
• Combine high-level and low-level synthesis
  techniques
• Learning techniques

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Grasp Force Optimization
Contact forces
Friction cone LMI
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