This work introduces a framework harnessing the capabilities of Large
Language Models (LLMs) to generate primitive task conditions for generalizable
long-horizon manipulations with novel objects and unseen tasks. These task
conditions serve as guides for the generation and adjustment of Dynamic
Movement Primitives (DMP) trajectories for long-horizon task execution. We
further create a challenging robotic manipulation task suite based on Pybullet
for long-horizon task evaluation. Extensive experiments in both simulated and
real-world environments demonstrate the effectiveness of our framework on both
familiar tasks involving new objects and novel but related tasks, highlighting
the potential of LLMs in enhancing robotic system versatility and adaptability.
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该研究介绍了一种利用大型语言模型（LLMs）的能力生成通用长视野操作的基本任务条件的框架，用于对新颖物体和未知任务进行操作。这些任务条件用于指导长时程任务执行中动态运动基元（DMP）轨迹的生成和调整。我们还基于 Pybullet 创建了一个用于进行长时程任务评估的具有挑战性的机器人操纵任务套件。在模拟和现实环境中进行的大量实验证明了我们的框架在涉及新对象的熟悉任务和新颖但相关任务上的有效性，突显了大型语言模型在提升机器人系统的多功能性和适应性方面的潜力。