  Sample efficiency is critical when applying learning-based methods to robotic
manipulation due to the high cost of collecting expert demonstrations and the
challenges of on-robot policy learning through online Reinforcement Learning
(RL). Offline RL addresses this issue by enabling policy learning from an
offline dataset collected using any behavioral policy, regardless of its
quality. However, recent advancements in offline RL have predominantly focused
on learning from large datasets. Given that many robotic manipulation tasks can
be formulated as rotation-symmetric problems, we investigate the use of
$SO(2)$-equivariant neural networks for offline RL with a limited number of
demonstrations. Our experimental results show that equivariant versions of
Conservative Q-Learning (CQL) and Implicit Q-Learning (IQL) outperform their
non-equivariant counterparts. We provide empirical evidence demonstrating how
equivariance improves offline learning algorithms in the low-data regime.
