ANNA is a simulation of human assistants who do not necessarily know themselves how to optimally accomplish the agent's goal: they are only familiar with scenes along certain paths in the environment, and thus give advice to help the agent make partial progress.
realistic simulators play an important role in sim-to-real approaches, in which an agent is trained with arbitrarily many samples provided by the simulators, then transferred to real settings using sample-efficient transfer learning techniques (Kalashnikov et al., 2018;Andrychowicz et al., 2018;Karttunen et al., 2019).