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. Project website: https://object814.github.io/Task-Condition-With-LLM/

翻译：本文提出了一种框架，利用大型语言模型（LLMs）为可泛化的长期操控任务生成基本任务条件，这些任务涉及新物体和未见过的任务。这些任务条件作为动态运动基元（DMP）轨迹生成与调整的引导，用于长期任务的执行。我们进一步基于Pybullet构建了一个具有挑战性的机器人操控任务套件，用于评估长期任务。在模拟和真实环境中的大量实验表明，我们的框架在处理涉及新物体的熟悉任务以及新颖但相关的任务时均有效，突显了LLMs在提升机器人系统多样性与适应性方面的潜力。项目网站：https://object814.github.io/Task-Condition-With-LLM/