多粒度语言引导的指令分解模仿学习
Multi-Granularity Language-Guided Imitation Learning via Instruction Decomposition
- National Cheng Kung University（国立成功大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有语言引导模仿学习难以区分多阶段任务不同子任务的问题，提出基于指令分解的多粒度语言引导方法，将整体描述分解为子任务级指令，提升学习效率与性能。
AI中文摘要：
近年来，使用语言指令作为条件来引导机器人策略学习已成为一个重要的研究领域。然而，现有的语言引导策略学习方法通常使用整体任务描述来引导整个示范轨迹。对于涉及多个执行阶段的操作任务，这些方法将相同的语言描述分配给不同的子任务，使得难以区分不同阶段所需的行为。在这项工作中，我们提出了一种基于指令分解的多粒度语言引导方法。所提出的方法将整体任务描述分解为更细粒度、具体的子任务级语言指令，从而提高学习效率并改善性能。我们在多任务模仿学习的设置下评估了所提出的方法，并验证了其有效性。
英文摘要：
Using language instructions as conditions to guide robot policy learning has recently become an important research domain. However, existing language-guided policy learning methods typically use an overall task description to guide the entire demonstration trajectory. For manipulation tasks involving multiple execution stages, these methods assign the same language description to different subtasks, making it difficult to distinguish the behaviors required at different stages. In this work, we propose a multi-granularity language guidance method based on instruction decomposition. The proposed method decomposes an overall task description into more fine-grained, concrete subtask-level language instructions, thereby enhancing learning efficiency and improving performance. We evaluate the proposed method in the setting of multi-task imitation learning and validate its effectiveness.
