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

arxiv.org · 7,037 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, existing approaches either translate the natural language directly into robot trajectories or factor the inference process by decomposing language into task sub-goals and 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 factorization into subgoals unt

AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers Yongchao Chen 1 , 2 1 2 {}^{1,2} start_FLOATSUPERSCRIPT 1 , 2 end_FLOATSUPERSCRIPT , Jacob Arkin 1 1 {}^{1} start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT , Charles Dawson 1 1 {}^{1} start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT , Yang Zhang 3 3 {}^{3} start_FLOATSUPERSCRIPT 3 end_FLOATSUPERSCRIPT , Nicholas Roy 1 1 {}^{1} start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT , and Chuchu Fan 1 1 {}^{1} start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT 1 1 {}^{1} start_FLOATSUPERSCRIPT 1 end_FLOATSUPERSCRIPT Massachuset

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