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.

LLM^3 是一种基于大型语言模型（LLM）的全新的任务和运动规划（TAMP）框架，具有领域无关的接口，利用预训练的 LLM 的强大推理和规划能力来提出符号动作序列并选择运动规划的连续动作参数，并通过提示将运动规划反馈到 LLM 中，从而通过对运动失败进行推理来不断改进提案，解决 TAMP 问题，选择行动参数的有效性得到定量证明，并在实际应用中对物理机械臂进行定性实验。