标题： 创始人：在世界模型中建立基础模型以实现开放式的具身决策 显示英文标题

标题： FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making

摘要： 基础模型（FMs）和世界模型（WMs）在不同层次的任务泛化中提供了互补的优势。 在本工作中，我们提出了FOUNDER，一种将FMs中嵌入的可泛化知识与WMs的动态建模能力相结合的框架，以在无奖励的情况下实现具身环境中的开放式任务求解。 我们学习一个映射函数，将FM表示接地到WM状态空间中，从而从外部观察中有效推断出代理在世界模拟器中的物理状态。 这种映射使得在行为学习过程中通过想象学习一个目标条件策略成为可能，其中映射后的任务作为目标状态。 我们的方法利用预测的目标状态的时间距离作为有信息量的奖励信号。 FOUNDER在各种多任务离线视觉控制基准测试中表现出色，特别是在捕捉由文本或视频指定的任务的深层语义方面表现突出，尤其是在涉及复杂观察或领域差距的场景中，先前的方法难以应对。 我们的学习奖励函数与真实奖励的一致性也通过实验得到了验证。 我们的项目网站是https://sites.google.com/view/founder-rl。 显示英文摘要

摘要： Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the world simulator from external observations. This mapping enables the learning of a goal-conditioned policy through imagination during behavior learning, with the mapped task serving as the goal state. Our method leverages the predicted temporal distance to the goal state as an informative reward signal. FOUNDER demonstrates superior performance on various multi-task offline visual control benchmarks, excelling in capturing the deep-level semantics of tasks specified by text or videos, particularly in scenarios involving complex observations or domain gaps where prior methods struggle. The consistency of our learned reward function with the ground-truth reward is also empirically validated. Our project website is https://sites.google.com/view/founder-rl.