Humanoid robot loco-manipulation remains constrained by the semantic-physical gap. Current methods face three limitations: Low sample efficiency in reinforcement learning, poor generalization in imitation learning, and physical inconsistency in VLMs. We propose MetaWorld, a hierarchical world model that integrates semantic planning and physical control via expert policy transfer. The framework decouples tasks into a VLM-driven semantic layer and a latent dynamics model operating in a compact state space. Our dynamic expert selection and motion prior fusion mechanism leverages a pre-trained multi-expert policy library as transferable knowledge, enabling efficient online adaptation via a two-stage framework. VLMs serve as semantic interfaces, mapping instructions to executable skills and bypassing symbol grounding. Experiments on Humanoid-Bench show MetaWorld outperforms world model-based RL in task completion and motion coherence. Our code will be found at https://anonymous.4open.science/r/metaworld-2BF4/
@article{arxiv.2601.17507,
title = {MetaWorld: Skill Transfer and Composition in a Hierarchical World Model for Grounding High-Level Instructions},
author = {Yutong Shen and Hangxu Liu and Kailin Pei and Ruizhe Xia and Tongtong Feng},
journal= {arXiv preprint arXiv:2601.17507},
year = {2026}
}
Comments
8 pages, 4 figures, Submitted to ICLR 2026 World Model Workshop