GET-Zero: Graph Embodiment Transformer for Zero-Shot Embodiment Generalization

Abstract

This paper introduces GET-Zero, a model architecture and training procedure for learning an embodimentaware 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 inhand 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 selfmodeling 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 our project website.