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.

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