Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, we present LLM^3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface. Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning. Crucially, LLM^3 incorporates motion planning feed- back through prompting, allowing the LLM to iteratively refine its proposals by reasoning about motion failure. Consequently, LLM^3 interfaces between task planning and motion planning, alleviating the intricate design process of handling domain- specific messages between them. Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters. Ablation studies un- derscore the significant contribution of motion failure reasoning to the success of LLM^3. Furthermore, we conduct qualitative experiments on a physical manipulator, demonstrating the practical applicability of our approach in real-world settings.

翻译：传统的任务与运动规划方法依赖于手工设计的接口，将符号化任务规划与连续运动生成相连接。这些领域特定且劳动密集型的模块在处理现实场景中的新兴任务时存在局限。本文提出LLM^3——一种新颖的基于大语言模型的任务与运动规划框架，其特点是采用领域无关的接口。具体而言，我们利用预训练大语言模型强大的推理与规划能力，提出符号化动作序列，并为运动规划选择连续动作参数。关键之处在于，LLM^3通过提示机制融入运动规划反馈，使大语言模型能够通过推理运动失败原因来迭代优化其提议。由此，LLM^3作为任务规划与运动规划的接口，简化了处理二者间领域特定消息的复杂设计流程。通过在箱体堆叠领域的一系列仿真实验，我们定量证明了LLM^3解决任务与运动规划问题的有效性及其在动作参数选择上的高效性。消融研究进一步凸显了运动失败推理对LLM^3成功的关键贡献。此外，我们在实体机械臂上开展定性实验，验证了该方法在现实场景中的实用价值。