用大模型生成机器人任务计划，更安全高效。
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
- 通过等价投票从自然语言生成多个逻辑公式，选多数一致的作为最终约束。
- 在生成计划时用逻辑公式强制约束，确保结果符合用户指令。
- 针对特定任务微调模型，提升安全性和执行效率，适合复杂长程任务。
尽管大型语言模型（LLMs）显著提升了机器人理解与执行自然语言命令的能力，但在复杂命令和长时序任务中，确保其遵守用户指定约束仍具挑战。为此，我们提出三项关键洞察：等价投票、约束解码和领域专用微调，显著增强LLM规划器处理复杂任务的能力。等价投票通过从自然语言命令生成并采样多个线性时序逻辑（LTL）公式，分组等价公式，并选取多数派作为最终LTL公式，保障一致性。约束解码利用生成的LTL公式，在自回归推理过程中强制规划符合约束，确保生成计划满足要求。领域专用微调则定制化模型，使其在特定任务域中生成更安全、高效的计划。我们提出的安全高效LLM规划器（SELP）融合这些方法，生成高置信度符合用户指令的计划。实验表明，SELP在不同机器人和任务中均表现出色，包括无人机导航与机器人操作。在无人机导航任务中，安全率提升10.8%，计划效率提高19.8%；在机器人操作任务中，安全率提升20.4%。相关数据集将发布于github.com/lt-asset/selp。
原文摘要 · Abstract (English)
Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adhere to user-specified constraints remains challenging, particularly for complex commands and long-horizon tasks. To address this challenge, we present three key insights, equivalence voting, constrained decoding, and domain-specific fine-tuning, which significantly enhance LLM planners' capability in handling complex tasks. Equivalence voting ensures consistency by generating and sampling multiple Linear Temporal Logic (LTL) formulas from NL commands, grouping equivalent LTL formulas, and selecting the majority group of formulas as the final LTL formula. Constrained decoding then uses the generated LTL formula to enforce the autoregressive inference of plans, ensuring the generated plans conform to the LTL. Domain-specific fine-tuning customizes LLMs to produce safe and efficient plans within specific task domains. Our approach, Safe Efficient LLM Planner (SELP), combines these insights to create LLM planners to generate plans adhering to user commands with high confidence. We demonstrate the effectiveness and generalizability of SELP across different robot agents and tasks, including drone navigation and robot manipulation. For drone navigation tasks, SELP outperforms state-of-the-art planners by 10.8% in safety rate (i.e., finishing tasks conforming to NL commands) and by 19.8% in plan efficiency. For robot manipulation tasks, SELP achieves 20.4% improvement in safety rate. Our datasets for evaluating NL-to-LTL and robot task planning will be released in github.com/lt-asset/selp.
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