  We present PoliFormer (Policy Transformer), an RGB-only indoor navigation
agent trained end-to-end with reinforcement learning at scale that generalizes
to the real-world without adaptation despite being trained purely in
simulation. PoliFormer uses a foundational vision transformer encoder with a
causal transformer decoder enabling long-term memory and reasoning. It is
trained for hundreds of millions of interactions across diverse environments,
leveraging parallelized, multi-machine rollouts for efficient training with
high throughput. PoliFormer is a masterful navigator, producing
state-of-the-art results across two distinct embodiments, the LoCoBot and
Stretch RE-1 robots, and four navigation benchmarks. It breaks through the
plateaus of previous work, achieving an unprecedented 85.5% success rate in
object goal navigation on the CHORES-S benchmark, a 28.5% absolute improvement.
PoliFormer can also be trivially extended to a variety of downstream
applications such as object tracking, multi-object navigation, and
open-vocabulary navigation with no finetuning.
