  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 $\mathrm{SE}(2)$. In this paper, we
recognize that the optimal grasp function is $\mathrm{SE}(2)$-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.
