  Multiple domains like vision, natural language, and audio are witnessing
tremendous progress by leveraging Transformers for large scale pre-training
followed by task specific fine tuning. In contrast, in robotics we primarily
train a single robot for a single task. However, modular robot systems now
allow for the flexible combination of general-purpose building blocks into task
optimized morphologies. However, given the exponentially large number of
possible robot morphologies, training a controller for each new design is
impractical. In this work, we propose MetaMorph, a Transformer based approach
to learn a universal controller over a modular robot design space. MetaMorph is
based on the insight that robot morphology is just another modality on which we
can condition the output of a Transformer. Through extensive experiments we
demonstrate that large scale pre-training on a variety of robot morphologies
results in policies with combinatorial generalization capabilities, including
zero shot generalization to unseen robot morphologies. We further demonstrate
that our pre-trained policy can be used for sample-efficient transfer to
completely new robot morphologies and tasks.
