We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified to reach inferred symbolic goals. Text2Motion uses feasibility heuristics encoded in Q-functions of a library of skills to guide task planning with Large Language Models. Whereas previous language-based planners only consider the feasibility of individual skills, Text2Motion actively resolves geometric dependencies spanning skill sequences by performing geometric feasibility planning during its search. We evaluate our method on a suite of problems that require long-horizon reasoning, interpretation of abstract goals, and handling of partial affordance perception. Our experiments show that Text2Motion can solve these challenging problems with a success rate of 82%, while prior state-of-the-art language-based planning methods only achieve 13%. Text2Motion thus provides promising generalization characteristics to semantically diverse sequential manipulation tasks with geometric dependencies between skills.

翻译：我们提出Text2Motion，一个基于语言的规划框架，使机器人能够解决需要长时域推理的序列操作任务。给定自然语言指令，该框架构建经过验证可达成符号化目标的、结合任务层与运动层的规划方案。Text2Motion利用技能库中Q函数编码的可行性启发式策略，引导大语言模型进行任务规划。相较于以往仅考虑单个技能可行性的语言规划器，Text2Motion在搜索过程中主动执行几何可行性规划，以解析跨技能序列的几何依赖关系。我们在需要长时域推理、抽象目标解读及部分功能感知处理的问题集上评估该方法的性能。实验表明，Text2Motion能以82%的成功率解决这些具有挑战性的问题，而先前最先进的基于语言的规划方法仅达13%。因此，Text2Motion为语义多样且具有技能间几何依赖关系的序列操作任务展现出极具前景的泛化特性。