  This paper introduces GET-Zero, a model architecture and training procedure
for learning an embodiment-aware control policy that can immediately adapt to
new hardware changes without retraining. To do so, we present Graph Embodiment
Transformer (GET), a transformer model that leverages the embodiment graph
connectivity as a learned structural bias in the attention mechanism. We use
behavior cloning to distill demonstration data from embodiment-specific expert
policies into an embodiment-aware GET model that conditions on the hardware
configuration of the robot to make control decisions. We conduct a case study
on a dexterous in-hand object rotation task using different configurations of a
four-fingered robot hand with joints removed and with link length extensions.
Using the GET model along with a self-modeling loss enables GET-Zero to
zero-shot generalize to unseen variation in graph structure and link length,
yielding a 20% improvement over baseline methods. All code and qualitative
video results are on https://get-zero-paper.github.io
