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 feedback 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 underscore 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：传统任务与运动规划方法依赖人工设计的接口来连接符号化任务规划与连续运动生成。这些领域特定且劳动密集型的模块在应对真实场景中的新兴任务时存在局限。本文提出LLM^3——一种基于大语言模型的新型任务与运动规划框架，其核心包含领域无关的接口。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，生成符号化动作序列并选取用于运动规划的连续动作参数。关键在于，LLM^3通过提示机制引入运动规划反馈，使大语言模型能够基于运动失败推理迭代优化方案。由此，LLM^3在任务规划与运动规划之间建立接口，消除了处理两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系列仿真实验，我们定量验证了LLM^3解决任务与运动规划问题的有效性及其选取动作参数的效率。消融研究凸显了运动失败推理对LLM^3成功的重要贡献。此外，我们还在实体机械臂上开展定性实验，证明该方法在真实场景中的实用可行性。