An SVM Learning Approach to Robotic Grasping Raphael Pelossof December 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 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 = SVM regression with RBF Kernel with different values for the variance, C, and epsilon.

Convergence Results Training DatasetTesting Dataset Test Error: 0.03 Training and testing over top 150 grasps per superquadric

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