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

arXiv:2306.06531v3 Announce Type: replace-cross
Abstract: 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 …

arxiv checkers cs.cl cs.hc cs.ro llms motion planning planning type