The importance of unsupervised adaptation at test time

The algorithm should be able to adapt to new environments without supervision at test time./nSelf-supervision requires fewer episodes for fine-tuning compared to using reward signals./nUsing multiple models with different objectives can be effective but dependent on the task./nCombining multiple self-supervised objectives can confuse the encoder./nFinding an automatic way to determine the most effective self-supervised objective is challenging./nThe complexity and frequency of real robot interaction can be practical constraints./nThe research interest after this point is not mentioned.