Empowering embodied agents, such as robots, with Artificial Intelligence (AI)
has become increasingly important in recent years. A major challenge is task
open-endedness. In practice, robots often need to perform tasks with novel
goals that are multifaceted, dynamic, lack a definitive "end-state", and were
not encountered during training. To tackle this problem, this paper introduces
\textit{Diffusion for Open-ended Goals} (DOG), a novel framework designed to
enable embodied AI to plan and act flexibly and dynamically for open-ended task
goals. DOG synergizes the generative prowess of diffusion models with
state-of-the-art, training-free guidance techniques to adaptively perform
online planning and control. Our evaluations demonstrate that DOG can handle
various kinds of novel task goals not seen during training, in both maze
navigation and robot control problems. Our work sheds light on enhancing
embodied AI's adaptability and competency in tackling open-ended goals.

通过引入 "Diffusion for Open-ended Goals"（DOG）框架来提升具有人工智能的体验智能体（如机器人）的能力，以处理未预见的多方面、动态的、缺乏明确定义的的开放式任务目标，并展示了 DOG 在迷宫导航和机器人控制问题中处理未见过的任务目标的能力，以增强体验智能体在处理开放式目标时的适应性和能力。