Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control

2026-08-21

AI Summary

为解决连续控制中形态参数变化导致的世界模型性能下降问题，提出Graph-Operator世界模型，通过将动力学分解为形态独立部分和形态依赖部分来提高泛化能力。

Abstract

World models for continuous control are commonly trained for a fixed physical system and can degrade when known morphology parameters such as link lengths, masses, damping, and actuation change. Existing approaches often provide these parameters as conditioning information, but leave unspecified which part of the learned transition should remain reusable and which part should change with morphology. We propose Graph-Operator World Models (GraphOp-WM), a structured world model for generalization across unseen morphology parameters within related articulated robot families. GraphOp-WM represents bodies and their kinematic relations as an attributed graph and factorizes each transition into a morphology-independent local dynamics basis and a morphology-conditioned structured operator. The operator combines node-local modulation, kinematic-tree coupling, and a low-rank global correction, while architectural information separation, basis normalization, and paired-morphology supervision encourage static morphology dependence to be carried by the operator pathway. Graph-level readout and edge-wise action representations provide a compatible interface for reward, value, and TD-MPC-style planning. We further define controlled MuJoCo parameter splits covering interpolation, extrapolation, and held-out compositions of link geometry, mass, damping, and actuation parameters in Hopper, Walker2d, and HalfCheetah.

Problem

• continuous control
• world models
• morphology parameters
• generalization
• structured operator

Innovation

• Graph-Operator World Models
• morphology-parameter generalization
• continuous control
• structured operator
• local dynamics basis