Transcript pelossof.ppt

An SVM Learning Approach to
Robotic Grasping
Raphael Pelossof
December 03 2002
Advanced Machine Learning
Introduction
•
Motivation:
Provide an easy method for optimizing a robotic
grasp for arbitrary hands.
•
Difficulty:
Grasping involves many degrees of freedom, and
the grasp quality surface is undefined
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Solution:
1. Sample grasp quality surface for different shapes
and hand configurations
2. Fit SVM regression to quality surface
3. Travel along kernel gradient to find optimal grasp
given a new shape.
The Barret Hand
• Barret hand has four DOF
– Spread angle (1)
– Finger rotation (3)
• Orientation in space has six DOF
– Translation in space (3)
– Rotation in space (3)
Superquadrics
• Superquadrics are a smooth family of shapes with
smooth transitions between them.
• Superellipse:
• Constant number of parameters
• Global deformations can be applied to them
• Surface normals
GraspIt!
• Grasp analysis simulator
– Approximats grasp quality measure using a 6D wrench space
– Different materials
– Normalized quality measure
• Grasp planning using superquadrics
• Simulation Time
SVM
• Semi - Monte Carlo sampling
• Angle representation
• Dataset Size: 9sq x 16vec x 100ang = 14400
• SVM regression with RBF Kernel
with different values for the
variance, C, and epsilon.
Convergence Results
Training and testing over top 150 grasps per superquadric
Training Dataset
Testing Dataset
Test Error: 0.03
Give me a shape I’ll find the best
grasp !
1. Modify all first two dimensions of all support
vectors to the requested parameters.
2. Evaluate SVM at all support vectors
3. Start from the support vector with the highest
value
4. Iterate until no improvement in quality
Future Work I
• Using Priors:
P(spread) ~ N(mu=1, sigma = 0.067)
• Change the distance metric
from Euclidean distance to
Geodesic over a sphere.
– Distances between angles live on a sphere
– Kernel no longer satisfies mercer’s condition
Future Work II
• Machine Vision to further constraint the
search space
• Use Convex Invariance Learning (CoIL) for
multiple superquadrics