  Successfully addressing a wide variety of tasks is a core ability of
autonomous agents, requiring flexibly adapting the underlying decision-making
strategies and, as we argue in this work, also adapting the perception modules.
An analogical argument would be the human visual system, which uses top-down
signals to focus attention determined by the current task. Similarly, we adapt
pre-trained large vision models conditioned on specific downstream tasks in the
context of multi-task policy learning. We introduce task-conditioned adapters
that do not require finetuning any pre-trained weights, combined with a single
policy trained with behavior cloning and capable of addressing multiple tasks.
We condition the visual adapters on task embeddings, which can be selected at
inference if the task is known, or alternatively inferred from a set of example
demonstrations. To this end, we propose a new optimization-based estimator. We
evaluate the method on a wide variety of tasks from the CortexBench benchmark
and show that, compared to existing work, it can be addressed with a single
policy. In particular, we demonstrate that adapting visual features is a key
design choice and that the method generalizes to unseen tasks given a few
demonstrations.
