Conformal Temporal Logic Planning using Large Language Models: Knowing When to Do What and When to Ask for Help

This paper addresses a new motion planning problem for mobile robots tasked with accomplishing multiple high-level sub-tasks, expressed using natural language (NL). These sub-tasks should be accomplished in a temporal and logical order. To formally define the overarching mission, we leverage Linear Temporal Logic (LTL) defined over atomic predicates modeling these NL-based sub-tasks. This is in contrast to related planning approaches that define LTL tasks over atomic predicates capturing desired low-level system configurations. Our goal is to design robot plans that satisfy LTL tasks defined over NL-based atomic propositions. A novel technical challenge arising in this setup lies in reasoning about correctness of a robot plan with respect to such LTL-encoded tasks. To address this problem, we propose HERACLEs, a hierarchical conformal natural language planner, that relies on (i) automata theory to determine what NL-specified sub-tasks should be accomplished next to make mission progress; (ii) Large Language Models to design robot plans satisfying these sub-tasks; and (iii) conformal prediction to reason probabilistically about correctness of the designed plans and to determine if external assistance is required. We provide theoretical probabilistic mission satisfaction guarantees as well as extensive comparative experiments on mobile manipulation tasks.