Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantitative Path-Planning from Qualitative Language Instructions
Authors: Daqing Yi
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The framework is being tested with a Turtlebot in an environment with landmarks. |
| Researcher Affiliation | Academia | Daqing Yi Department of Computer Science, Brigham Young University, Provo, Utah EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper mentions testing with a Turtlebot in an environment but does not specify any publicly available datasets used for training or otherwise provide access information for a dataset. |
| Dataset Splits | No | The paper does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | The paper mentions using a "Turtlebot" but does not specify any detailed hardware components like CPU, GPU, or memory used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes its algorithms and approach but does not provide specific experimental setup details such as hyperparameters, learning rates, or other configuration settings. |