Summary of Nsp: a Neuro-symbolic Natural Language Navigational Planner, by William English et al.
NSP: A Neuro-Symbolic Natural Language Navigational Planner
by William English, Dominic Simon, Sumit Jha, Rickard Ewetz
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a neuro-symbolic framework for path planning from natural language inputs, called NSP. This framework combines the strengths of Large Language Models (LLMs) and symbolic approaches to efficiently plan paths while ensuring correctness. The LLMs generate symbolic representations of the environment, which are then used by a symbolic path planning algorithm. A feedback loop ensures that syntax errors are corrected and execution time constraints are satisfied. The proposed approach is evaluated on a benchmark suite with 1500 path-planning problems, showing an average 19-77% reduction in path length compared to state-of-the-art neural approaches, while producing 90.1% valid paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to control robots using simple language instructions. This paper develops a new way for robots to understand and follow natural language commands. They combine two types of intelligence: one that can process language like humans do, and another that can create detailed maps of the environment. By working together, these two parts can plan the best path for the robot to follow. The team tested this approach on many problems and found that it works very well, producing paths that are shorter than what other approaches would find. |
Keywords
» Artificial intelligence » Syntax
