Like last year's challenge, the Real Robot Challenge III is featured in the NeurIPS 2022 Competition Track... Learning manipulation policies on real robots is usually quite costly and challenging. Offline RL, however, has the potential to mitigate this problem by making use of precollected data. Yet, opportunities for researchers to work with such datasets and to then evaluate learned policies on real-robot hardware are scarce. For this reason, a large part of the 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 purpose of this competition is to alleviate this problem by allowing participants to experiment remotely with a real robot -- as easily as in simulation... The goal of this year's challenge is to solve dexterous manipulation tasks with offline reinforcement learning (RL) or imitation learning. The..
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