arXiv:2409.06859v1 Announce Type: new
Abstract: 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 guarantees. 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.
The Future of Robotics: Neuro-Symbolic Path Planning with Natural Language
Path planning is a critical task in robotics that involves finding the optimal path for a robot to navigate its environment. Traditionally, path planning algorithms have relied on predefined rules and symbolic representations of the environment. However, these approaches struggle to interpret free-form natural language instructions, which limits their usability in real-world applications.
On the other hand, neural approaches based on Large Language Models (LLMs) have shown promise in understanding natural language inputs. These models can handle complex sentence structures and generate coherent responses. However, they lack performance guarantees and may not always produce accurate results.
In this groundbreaking paper, the authors propose a neuro-symbolic framework called NSP for path planning from natural language inputs. This framework combines the best of both worlds by leveraging the neural reasoning abilities of LLMs and the guarantees provided by symbolic approaches.
The NSP framework consists of two main components:
- Neural Reasoning: The LLMs are used to convert natural language instructions into symbolic representations of the environment. This allows the algorithm to understand and reason about the spatial relationships between different objects in the environment.
- Symbolic Path Planning: Once the symbolic representation of the environment is obtained, a symbolic path planning algorithm is executed to find the optimal path for the robot. This algorithm takes into account various factors such as obstacles, goal location, and the robot’s capabilities.
One of the key strengths of the NSP framework is its ability to self-correct syntax errors and satisfy execution time constraints. By using a feedback loop from the symbolic execution environment to the neural generation process, the framework can refine its understanding of the natural language instructions and improve the accuracy of the path planning algorithm.
The authors evaluated the NSP framework using a benchmark suite with 1500 path-planning problems. The results were impressive, with the neuro-symbolic approach producing 90.1% valid paths that were on average 19-77% shorter than state-of-the-art neural approaches.
This research highlights the multi-disciplinary nature of robotics and the potential of combining symbolic and neural approaches. By integrating natural language processing, reasoning, and path planning algorithms, the NSP framework opens up new possibilities for intuitive and efficient control of complex semi-autonomous systems.
The implications of this neuro-symbolic approach extend beyond robotics. The ability to interpret free-form natural language instructions could have applications in various domains such as virtual assistants, autonomous vehicles, and smart home systems. As the field evolves, we can expect to see further advancements in the fusion of symbolic and neural techniques, enabling even more sophisticated and intelligent systems.