  Deploying machine learning algorithms for robot tasks in real-world
applications presents a core challenge: overcoming the domain gap between the
training and the deployment environment. This is particularly difficult for
visuomotor policies that utilize high-dimensional images as input, particularly
when those images are generated via simulation. A common method to tackle this
issue is through domain randomization, which aims to broaden the span of the
training distribution to cover the test-time distribution. However, this
approach is only effective when the domain randomization encompasses the actual
shifts in the test-time distribution. We take a different approach, where we
make use of a single demonstration (a prompt) to learn policy that adapts to
the testing target environment. Our proposed framework, PromptAdapt, leverages
the Transformer architecture's capacity to model sequential data to learn
demonstration-conditioned visual policies, allowing for in-context adaptation
to a target domain that is distinct from training. Our experiments in both
simulation and real-world settings show that PromptAdapt is a strong
domain-adapting policy that outperforms baseline methods by a large margin
under a range of domain shifts, including variations in lighting, color,
texture, and camera pose. Videos and more information can be viewed at project
webpage: https://sites.google.com/view/promptadapt.
