  Enabling robots to learn novel visuomotor skills in a data-efficient manner
remains an unsolved problem with myriad challenges. A popular paradigm for
tackling this problem is through leveraging large unlabeled datasets that have
many behaviors in them and then adapting a policy to a specific task using a
small amount of task-specific human supervision (i.e. interventions or
demonstrations). However, how best to leverage the narrow task-specific
supervision and balance it with offline data remains an open question. Our key
insight in this work is that task-specific data not only provides new data for
an agent to train on but can also inform the type of prior data the agent
should use for learning. Concretely, we propose a simple approach that uses a
small amount of downstream expert data to selectively query relevant behaviors
from an offline, unlabeled dataset (including many sub-optimal behaviors). The
agent is then jointly trained on the expert and queried data. We observe that
our method learns to query only the relevant transitions to the task, filtering
out sub-optimal or task-irrelevant data. By doing so, it is able to learn more
effectively from the mix of task-specific and offline data compared to naively
mixing the data or only using the task-specific data. Furthermore, we find that
our simple querying approach outperforms more complex goal-conditioned methods
by 20% across simulated and real robotic manipulation tasks from images. See
https://sites.google.com/view/behaviorretrieval for videos and code.
