  In the field of robotic control, designing individual controllers for each
robot leads to high computational costs. Universal control policies, applicable
across diverse robot morphologies, promise to mitigate this challenge.
Predominantly, models based on Graph Neural Networks (GNN) and Transformers are
employed, owing to their effectiveness in capturing relational dynamics across
a robot's limbs. However, these models typically employ homogeneous graph
structures that overlook the functional diversity of different limbs. To bridge
this gap, we introduce HeteroMorpheus, a novel method based on heterogeneous
graph Transformer. This method uniquely addresses limb heterogeneity, fostering
better representation of robot dynamics of various morphologies. Through
extensive experiments we demonstrate the superiority of HeteroMorpheus against
state-of-the-art methods in the capability of policy generalization, including
zero-shot generalization and sample-efficient transfer to unfamiliar robot
morphologies.
