Learning skills from language provides a powerful avenue for generalization in reinforcement learning, although it remains a challenging task as it requires agents to capture the complex interdependencies between language, actions, and states. In this paper, we propose leveraging Language Augmented Diffusion models as a planner conditioned on language (LAD). We demonstrate the comparable performance of LAD with the state-of-the-art on the CALVIN language robotics benchmark with a much simpler architecture that contains no inductive biases specialized to robotics, achieving an average success rate (SR) of 72% compared to the best performance of 76%. We also conduct an analysis on the properties of language conditioned diffusion in reinforcement learning.

翻译：从语言上学习技能是普及强化学习的有力途径,尽管这仍然是一项艰巨的任务,因为它需要各种力量来捕捉语言、行动和邦之间的复杂相互依存关系。在本文中,我们提议利用语言增强传播模式作为语言规划者(LAD),我们展示了LAD与CALVIN语言机器人基准最先进的技术相比的成绩,其结构简单得多,没有机器人专用的感应偏差,平均成功率(SR)为72%,而最佳成绩为76%。我们还分析了强化学习中有条件传播语言的特性。