Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control
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 ...
| Authors | Xu Yang, Yiqin Yang, Qianchuan Zhao |
|---|---|
| Primary category | cs.AI |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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