  Experimentation on real robots is demanding in terms of time and costs. For
this reason, a large part of the reinforcement learning (RL) community uses
simulators to develop and benchmark algorithms. However, insights gained in
simulation do not necessarily translate to real robots, in particular for tasks
involving complex interactions with the environment. The Real Robot Challenge
2022 therefore served as a bridge between the RL and robotics communities by
allowing participants to experiment remotely with a real robot - as easily as
in simulation.
  In the last years, offline reinforcement learning has matured into a
promising paradigm for learning from pre-collected datasets, alleviating the
reliance on expensive online interactions. We therefore asked the participants
to learn two dexterous manipulation tasks involving pushing, grasping, and
in-hand orientation from provided real-robot datasets. An extensive software
documentation and an initial stage based on a simulation of the real set-up
made the competition particularly accessible. By giving each team plenty of
access budget to evaluate their offline-learned policies on a cluster of seven
identical real TriFinger platforms, we organized an exciting competition for
machine learners and roboticists alike.
  In this work we state the rules of the competition, present the methods used
by the winning teams and compare their results with a benchmark of
state-of-the-art offline RL algorithms on the challenge datasets.
