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Experimentation on real robots is demanding in terms of time and costs. Forthis reason, a large part of the reinforcement learning (RL) community usessimulators to develop and benchmark algorithms. However, insights gained insimulation do not necessarily translate to real robots, in particular for tasksinvolving complex interactions with the environment. The Real Robot Challenge2022 therefore served as a bridge between the RL and robotics communities byallowing participants to experiment remotely with a real robot - as easily asin simulation.  In the last years, offline reinforcement learning has matured into apromising paradigm for learning from pre-collected datasets, alleviating thereliance on expensive online interactions. We therefore asked the participantsto learn two dexterous manipulation tasks involving pushing, grasping, andin-hand orientation from provided real-robot datasets. An extensive softwaredocumentation and an initial stage based on a simulation of the real set-upmade the competition particularly accessible. By giving each team plenty ofaccess budget to evaluate their offline-learned policies on a cluster of sevenidentical real TriFinger platforms, we organized an exciting competition formachine learners and roboticists alike.  In this work we state the rules of the competition, present the methods usedby the winning teams and compare their results with a benchmark ofstate-of-the-art offline RL algorithms on the challenge datasets.

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World