  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 involves five different PVRs, each trained for five distinct
manipulation or indoor navigation tasks. We performed this evaluation using
three different robots and two different policy learning paradigms. 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.
