HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling

TL;DR

HeteroMorpheus introduces a heterogeneous graph Transformer for universal robotic control, effectively capturing limb diversity to improve policy generalization across various robot morphologies.

Contribution

It presents a novel heterogeneous graph Transformer model that explicitly models limb heterogeneity, enhancing the generalization of control policies across diverse robot types.

Findings

Outperforms state-of-the-art methods in policy generalization

Enables zero-shot transfer to unseen robot morphologies

Demonstrates sample-efficient adaptation to new robots

Abstract

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.