Conformal Temporal Logic Planning using Large Language Models
- URL: http://arxiv.org/abs/2309.10092v3
- Date: Thu, 22 Feb 2024 21:48:12 GMT
- Title: Conformal Temporal Logic Planning using Large Language Models
- Authors: Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis
Kantaros
- Abstract summary: This paper addresses a new motion planning problem for mobile robots tasked with accomplishing multiple high-level sub-tasks.
These sub-tasks should be accomplished in a temporal and logical order.
Our goal is to design robot plans that satisfy tasks defined over NL-based atomic propositions.
- Score: 29.57952582715011
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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