Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards
Venue
2016 IEEE International Conference on Robotics and Automation (ICRA) (2016)
Publication Year
2016
Authors
Jeffrey Mahler, Florian T. Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai Kohlhoff, Torsten Kroeger, James Kuffner, Ken Goldberg
BibTeX
Abstract
This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object
models and a sampling-based planning algorithm to explore how Cloud Robotics can be
used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with
correlated rewards to leverage prior grasps and 3D object models in a growing
dataset that currently includes over 10,000 unique 3D object models and 2.5 million
parallel-jaw grasps. Each grasp includes an estimate of the probability of force
closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses
Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for
3D object classification, to provide a similarity metric between objects, and the
Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing
experiment runtime by up to three orders of magnitude. Experiments suggest that
correlated bandit techniques can use a cloud-based network of object models to
significantly reduce the number of samples required for robust grasp planning. We
report on system sensitivity to variations in similarity metrics and in uncertainty
in pose and friction. Code and updated information is available at
http://berkeleyautomation.github.io/dex-net/.
