  Recent progress on vision-language foundation models have brought significant
advancement to building general-purpose robots. By using the pre-trained models
to encode the scene and instructions as inputs for decision making, the
instruction-conditioned policy can generalize across different objects and
tasks. While this is encouraging, the policy still fails in most cases given an
unseen task or environment. In this work, we propose Policy Adaptation from
Foundation model Feedback (PAFF). When deploying the trained policy to a new
task or a new environment, we first let the policy play with randomly generated
instructions to record the demonstrations. While the execution could be wrong,
we can use the pre-trained foundation models to provide feedback to relabel the
demonstrations. This automatically provides new pairs of
demonstration-instruction data for policy fine-tuning. We evaluate our method
on a broad range of experiments with the focus on generalization on unseen
objects, unseen tasks, unseen environments, and sim-to-real transfer. We show
PAFF improves baselines by a large margin in all cases. Our project page is
available at https://geyuying.github.io/PAFF/

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics