In recent years, domains such as natural language processing and image
recognition have popularized the paradigm of using large datasets to pretrain
representations that can be effectively transferred to downstream tasks. In
this work we evaluate how such a paradigm should be done in imitation learning,
where both pretraining and finetuning data are trajectories collected by
experts interacting with an unknown environment. Namely, we consider a setting
where the pretraining corpus consists of multitask demonstrations and the task
for each demonstration is set by an unobserved latent context variable. The
goal is to use the pretraining corpus to learn a low dimensional representation
of the high dimensional (e.g., visual) observation space which can be
transferred to a novel context for finetuning on a limited dataset of
demonstrations. Among a variety of possible pretraining objectives, we argue
that inverse dynamics modeling -- i.e., predicting an action given the
observations appearing before and after it in the demonstration -- is
well-suited to this setting. We provide empirical evidence of this claim
through evaluations on a variety of simulated visuomotor manipulation problems.
While previous work has attempted various theoretical explanations regarding
the benefit of inverse dynamics modeling, we find that these arguments are
insufficient to explain the empirical advantages often observed in our
settings, and so we derive a novel analysis using a simple but general
environment model.

本研究在模仿学习中评估了如何使用大型数据集进行预训练。我们提供了关于使用多任务演示和逆动力学建模的语境下进行预训练的证据，并在各种虚拟视觉操作问题中评估了这些证据。