Conformal Temporal Logic Planning using Large Language Models
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915497616867328 |
|---|---|
| author | Wang, Jun Tong, Jiaming Tan, Kaiyuan Vorobeychik, Yevgeniy Kantaros, Yiannis |
| author_facet | Wang, Jun Tong, Jiaming Tan, Kaiyuan Vorobeychik, Yevgeniy Kantaros, Yiannis |
| contents | This paper addresses planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a temporal and logical order. To formally define the mission, we treat these sub-tasks as atomic predicates in a Linear Temporal Logic (LTL) formula. We refer to this task specification framework as LTL-NL. Our goal is to design plans, defined as sequences of robot actions, accomplishing LTL-NL tasks. This action planning problem cannot be solved directly by existing LTL planners because of the NL nature of atomic predicates. To address it, we propose HERACLEs, a hierarchical neuro-symbolic planner that relies on a novel integration of (i) existing symbolic planners generating high-level task plans determining the order at which the NL sub-tasks should be accomplished; (ii) pre-trained Large Language Models (LLMs) to design sequences of robot actions based on these task plans; and (iii) conformal prediction acting as a formal interface between (i) and (ii) and managing uncertainties due to LLM imperfections. We show, both theoretically and empirically, that HERACLEs can achieve user-defined mission success rates. Finally, we provide comparative experiments demonstrating that HERACLEs outperforms LLM-based planners that require the mission to be defined solely using NL. Additionally, we present examples demonstrating that our approach enhances user-friendliness compared to conventional symbolic approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10092 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Conformal Temporal Logic Planning using Large Language Models Wang, Jun Tong, Jiaming Tan, Kaiyuan Vorobeychik, Yevgeniy Kantaros, Yiannis Robotics Artificial Intelligence This paper addresses planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a temporal and logical order. To formally define the mission, we treat these sub-tasks as atomic predicates in a Linear Temporal Logic (LTL) formula. We refer to this task specification framework as LTL-NL. Our goal is to design plans, defined as sequences of robot actions, accomplishing LTL-NL tasks. This action planning problem cannot be solved directly by existing LTL planners because of the NL nature of atomic predicates. To address it, we propose HERACLEs, a hierarchical neuro-symbolic planner that relies on a novel integration of (i) existing symbolic planners generating high-level task plans determining the order at which the NL sub-tasks should be accomplished; (ii) pre-trained Large Language Models (LLMs) to design sequences of robot actions based on these task plans; and (iii) conformal prediction acting as a formal interface between (i) and (ii) and managing uncertainties due to LLM imperfections. We show, both theoretically and empirically, that HERACLEs can achieve user-defined mission success rates. Finally, we provide comparative experiments demonstrating that HERACLEs outperforms LLM-based planners that require the mission to be defined solely using NL. Additionally, we present examples demonstrating that our approach enhances user-friendliness compared to conventional symbolic approaches. |
| title | Conformal Temporal Logic Planning using Large Language Models |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2309.10092 |