For effective human-robot interaction, robots need to understand, plan, and
execute complex, long-horizon tasks described by natural language. The recent
and remarkable advances in large language models (LLMs) have shown promise for
translating natural language into robot action sequences for complex tasks.
However, many existing approaches either translate the natural language
directly into robot trajectories, or factor the inference process by
decomposing language into task sub-goals, then relying on a motion planner to
execute each sub-goal. When complex environmental and temporal constraints are
involved, inference over planning tasks must be performed jointly with motion
plans using traditional task-and-motion planning (TAMP) algorithms, making such
factorization untenable. Rather than using LLMs to directly plan task
sub-goals, we instead perform few-shot translation from natural language task
descriptions to an intermediary task representation that can then be consumed
by a TAMP algorithm to jointly solve the task and motion plan. To improve
translation, we automatically detect and correct both syntactic and semantic
errors via autoregressive re-prompting, resulting in significant improvements
in task completion. We show that our approach outperforms several methods using
LLMs as planners in complex task domains.

该研究提出了一种基于大型语言模型和任务运动规划的方法，以实现机器人对自然语言任务描述的理解和执行。该方法可以检测并纠正语法和语义错误，并在复杂任务领域中优于使用大型语言模型作为规划器的其他方法。

AutoTAMP: 基于 LLMs 的自回归任务和运动规划

AutoTAMP: Autoregressive Task and Motion Planning with LLMs as  Translators and Checkers

We introduce a visually-guided and physics-driven task-and-motion planning
benchmark, which we call the ThreeDWorld Transport Challenge. In this
challenge, an embodied agent equipped with two 9-DOF articulated arms is
spawned randomly in a simulated physical home environment. The agent is
required to find a small set of objects scattered around the house, pick them
up, and transport them to a desired final location. We also position containers
around the house that can be used as tools to assist with transporting objects
efficiently. To complete the task, an embodied agent must plan a sequence of
actions to change the state of a large number of objects in the face of
realistic physical constraints. We build this benchmark challenge using the
ThreeDWorld simulation: a virtual 3D environment where all objects respond to
physics, and where can be controlled using fully physics-driven navigation and
interaction API. We evaluate several existing agents on this benchmark.
Experimental results suggest that: 1) a pure RL model struggles on this
challenge; 2) hierarchical planning-based agents can transport some objects but
still far from solving this task. We anticipate that this benchmark will
empower researchers to develop more intelligent physics-driven robots for the
physical world.

介绍了三维仿真的机器人任务与动作规划基准测试的设计，通过对象的选择、拾取和运输，要求具有先进物理约束和规划任务的人工智能智能体能够在仿真环境中完成任务。实验结果表明，现有算法在此测试中需要进一步发展。