We present a large empirical investigation on the use of pre-trained visual
representations (PVRs) for training downstream policies that execute real-world
tasks. Our study spans five different PVRs, two different policy-learning
paradigms (imitation and reinforcement learning), and three different robots
for 5 distinct manipulation and indoor navigation tasks. From this effort, we
can arrive at three insights: 1) the performance trends of PVRs in the
simulation are generally indicative of their trends in the real world, 2) the
use of PVRs enables a first-of-its-kind result with indoor ImageNav (zero-shot
transfer to a held-out scene in the real world), and 3) the benefits from
variations in PVRs, primarily data-augmentation and fine-tuning, also transfer
to the real-world performance. See project website for additional details and
visuals.

大规模实证研究表明预训练视觉表示（PVRs）的使用对训练执行现实任务的下游策略非常有用，尤其在操作和室内导航任务中表现出明显的性能优势。