  Recently, equivariant neural network models have been shown to improve sample
efficiency for tasks in computer vision and reinforcement learning. This paper
explores this idea in the context of on-robot policy learning in which a policy
must be learned entirely on a physical robotic system without reference to a
model, a simulator, or an offline dataset. We focus on applications of
Equivariant SAC to robotic manipulation and explore a number of variations of
the algorithm. Ultimately, we demonstrate the ability to learn several
non-trivial manipulation tasks completely through on-robot experiences in less
than an hour or two of wall clock time.
