Sample Efficient Grasp Learning Using Equivariant Models
Jun 27, 2022·,,
,,·
1 min read
Xupeng Zhu
Dian Wang
Ondrej Biza
Guanang Su
Robin Walters
Robert Platt
Abstract
In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in SE(2). In this paper, we recognize that the optimal grasp function is SE(2)-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.
Type
Publication
In Robotics:Science and Systems (RSS 2022), New York, USA. (Also presented in ICRA 2022 Workshop on Scaling Robot Learning, Spotlight)
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.
Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.
