  Robots are traditionally bounded by a fixed embodiment during their
operational lifetime, which limits their ability to adapt to their
surroundings. Co-optimizing control and morphology of a robot, however, is
often inefficient due to the complex interplay between the controller and
morphology. In this paper, we propose a learning-based control method that can
inherently take morphology into consideration such that once the control policy
is trained in the simulator, it can be easily deployed to robots with different
embodiments in the real world. In particular, we present the Embodiment-aware
Transformer (EAT), an architecture that casts this control problem as
conditional sequence modeling. EAT outputs the optimal actions by leveraging a
causally masked Transformer. By conditioning an autoregressive model on the
desired robot embodiment, past states, and actions, our EAT model can generate
future actions that best fit the current robot embodiment. Experimental results
show that EAT can outperform all other alternatives in embodiment-varying
tasks, and succeed in an example of real-world evolution tasks: stepping down a
stair through updating the morphology alone. We hope that EAT will inspire a
new push toward real-world evolution across many domains, where algorithms like
EAT can blaze a trail by bridging the field of evolutionary robotics and big
data sequence modeling.
