让大模型直接指挥机器人完成复杂操作，无需为不同机械臂重写代码。
Lang2Manip: A Tool for LLM-Based Symbolic-to-Geometric Planning for Manipulation
- 用大模型理解自然语言指令生成符号动作
- 对接Kautham框架自动规划无碰撞轨迹
- 一套系统适配多种机器人和任务类型
仿真在机器人操作系统的开发中至关重要，尤其在任务与运动规划（TAMP）中，符号推理需与几何、运动学及物理执行相结合。尽管大语言模型（LLMs）已能从自然语言生成符号计划，但将其在仿真中执行常需针对特定机器人进行工程开发或依赖特定规划器集成。本文提出统一管道，将基于大模型的符号规划器与Kautham运动规划框架连接，实现可泛化的、与机器人无关的符号到几何操作。Kautham支持多种工业机械臂的ROS接口，提供单一接口下的几何、运动学动力学、物理驱动及约束式运动规划。本系统将语言指令转换为符号动作，并使用Kautham任意规划器计算并执行无碰撞轨迹，无需额外编码。结果是灵活可扩展的语言驱动TAMP工具，可跨机器人、规划模式和操作任务通用。
原文摘要 · Abstract (English)
Simulation is essential for developing robotic manipulation systems, particularly for task and motion planning (TAMP), where symbolic reasoning interfaces with geometric, kinematic, and physics-based execution. Recent advances in Large Language Models (LLMs) enable robots to generate symbolic plans from natural language, yet executing these plans in simulation often requires robot-specific engineering or planner-dependent integration. In this work, we present a unified pipeline that connects an LLM-based symbolic planner with the Kautham motion planning framework to achieve generalizable, robot-agnostic symbolic-to-geometric manipulation. Kautham provides ROS-compatible support for a wide range of industrial manipulators and offers geometric, kinodynamic, physics-driven, and constraint-based motion planning under a single interface. Our system converts language instructions into symbolic actions and computes and executes collision-free trajectories using any of Kautham's planners without additional coding. The result is a flexible and scalable tool for language-driven TAMP that is generalized across robots, planning modalities, and manipulation tasks.
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