Hierarchical Sampling-based Planner with LTL Constraints and Text Prompting

TL;DR

This paper presents a hierarchical planning framework that combines LTL constraints with natural language instructions, enabling robots to perform complex, goal-oriented navigation tasks while respecting safety and task specifications.

Contribution

The work introduces a novel hierarchical planner that integrates LTL constraints with text prompts, translating natural language into formal specifications for robot motion planning.

Findings

Effective high-level planning using LTL and text prompts

Integration of BFS with low-level RRT and PRM planners

Demonstrated adaptability to various task complexities

Abstract

This project introduces a hierarchical planner integrating Linear Temporal Logic (LTL) constraints with natural language prompting for robot motion planning. The framework decomposes maps into regions, generates directed graphs, and converts them into transition systems for high-level planning. Text instructions are translated into LTL formulas and converted to Deterministic Finite Automata (DFA) for sequential goal-reaching tasks while adhering to safety constraints. High-level plans, derived via Breadth-First Search (BFS), guide low-level planners like Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) for obstacle-avoidant navigation along with LTL tasks. The approach demonstrates adaptability to various task complexities, though challenges such as graph construction overhead and suboptimal path generation remain. Future directions include extending to considering terrain conditions and incorporating higher-order dynamics.