SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models

TL;DR

SELP enhances large language models for robot task planning by integrating equivalence voting, constrained decoding, and domain-specific fine-tuning, resulting in safer and more efficient plans across various robot tasks.

Contribution

This paper introduces SELP, a novel framework combining multiple techniques to improve LLM-based robot planning, ensuring safety and efficiency in complex, long-horizon tasks.

Findings

SELP outperforms state-of-the-art planners in safety and efficiency for drone navigation.

SELP achieves a 20.4% improvement in safety rate for robot manipulation tasks.

The approach is validated across different robot agents and tasks, demonstrating generalizability.

Abstract

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.