外观
论文arXiv 2605.000801 个月前
机器人学习的世界模型：一项全面综述
原文链接：World model for robot learning: A comprehensive survey（arXiv 2605.00080 · 发布日期：2026-09-28）
World model for robot learning: A comprehensive survey
该综述针对机器人学习中世界模型文献分散的问题，从机器人学习视角梳理其架构、功能角色与具身应用。
核心要点
- 动机是世界模型已成机器人学习核心组件，但相关文献在架构、功能和应用域上分散。
- 方法上综述世界模型与机器人策略的耦合、作为学习仿真器的用途，以及机器人视频世界模型演进。
- 内容还总结代表性数据集、基准和评估协议，并梳理关键范式、应用、挑战与未来方向。
- 作者将维护并定期更新配套 GitHub 仓库，汇集新工作、基准和相关资源。
英文摘要 (Abstract)
World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planning, simulation, evaluation, data generation, and have advanced rapidly with the rise of foundation models and large-scale video generation. However, the literature remains fragmented across architectures, functional roles, and embodied application domains. To address this gap, we present a comprehensive review of world models from a robot-learning perspective. We examine how world models are coupled with robot policies, how they serve as learned simulators for reinforcement learning and evaluation, and how robotic video world models have progressed from imagination-based generation to controllable, structured, and foundation-scale formulations. We further summarize representative datasets, benchmarks, and evaluation protocols. Overall, this survey systematically reviews the rapidly growing literature on world models for robot learning, clarifies key paradigms and applications, and highlights major challenges and future directions for predictive modeling in embodied agents. To facilitate continued access to newly emerging works, benchmarks, and resources, we will maintain and regularly update the accompanying GitHub repository on https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy alongside this survey.
作者：Bohan Hou, Gen K. Li, Jindou Jia, Tuo An, Xinying Guo, Sicong Leng, Haoran Geng, Yanjie Ze, Tatsuya Harada, Philip H. S. Torr
来源
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