Détail du document
Identifiant
oai:arXiv.org:2408.01230
Sujet
Computer Science - Robotics Computer Science - Machine Learnin...Auteur
Hao, YiFan Yang, Yang Song, Junru Peng, Wei Zhou, Weien Jiang, Tingsong Yao, WenCatégorie
Computer Science
Année
2024
Date de référencement
07/08/2024
Mots clés
based graph morphologies heteromorpheus controlRésumé
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.
Hao, YiFan,Yang, Yang,Song, Junru,Peng, Wei,Zhou, Weien,Jiang, Tingsong,Yao, Wen, 2024, HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling
MELAS: Phenotype Classification into Classic-versus-Atypical Presentations
presentations mitochondrial strokelike patients variability phenotype clinical melas
