WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world models for diverse robotic systems, they struggle to scale to a large number of distinct system dynamics and overlook domain knowledge of physical structures. To address these limitations, we introduce WestWorld, a knoWledge-Encoded Scalable Trajectory World model for diverse robotic systems. To tackle the scalability challenge, we propose a novel system-aware Mixture-of-Experts (Sys-MoE) that dynamically combines and routes specialized experts for different robotic systems via a learnable system embedding. To further enhance zero-shot generali...
| Authors | Yuchen Wang, Jiangtao Kong, Sizhe Wei et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 19 May 2026 |
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