NSP: A Neuro-Symbolic Natural Language Navigational Planner

• Published in: Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1289 - 1294
• Main Authors: English, William, Simon, Dominic, Jha, Sumit Kumar, Ewetz, Rickard
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.12.2024
• Subjects: Feedback loop; LLM; Natural languages; Navigation; Neuro-Symbolic AI; Path planning; Planning; Programming; Python; Robots; Spatial Reasoning; Syntactics; Time factors
• ISSN: 1946-0759
• DOI: 10.1109/ICMLA61862.2024.00201

Path planners that can interpret free-form natural language instructions hold promise to automate a wide range of robotics applications. These planners simplify user interactions and enable intuitive control over complex semi-autonomous systems. While existing symbolic approaches offer guarantees on the correctness and efficiency, they struggle to parse free-form natural language inputs. Conversely, neural approaches based on pre-trained Large Language Models (LLMs) can manage natural language inputs but lack performance guaran-tees. In this paper, we propose a neuro-symbolic framework for path planning from natural language inputs called NSP. The framework leverages the neural reasoning abilities of LLMs to i) craft symbolic representations of the environment and ii) a symbolic path planning algorithm. Next, a solution to the path planning problem is obtained by executing the algorithm on the environment representation. The framework uses a feedback loop from the symbolic execution environment to the neural generation process to self-correct syntax errors and satisfy execution time constraints. We evaluate our neuro-symbolic approach using a benchmark suite with 1500 path-planning problems. The experimental evaluation shows that our neuro-symbolic approach produces 90.1% valid paths that are on average 19-77% shorter than state-of-the-art neural approaches.