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..
Multi-Robot Coordination and Layout Design for Automated Warehousing
Authors: Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate MAP-Elites and DSAGE and compare the optimized layouts with the human-designed ones. |
| Researcher Affiliation | Academia | Yulun Zhang1 , Matthew C. Fontaine2 , Varun Bhatt2 , Stefanos Nikolaidis2 and Jiaoyang Li1 1Robotics Institute, Carnegie Mellon University 2Department of Computer Science, University of Southern California |
| Pseudocode | No | The paper includes diagrams (Figure 3, Figure 8) illustrating the approach but does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We include the source code of the experiments in: https://github. com/lunjohnzhang/warehouse env gen public |
| Open Datasets | No | The paper describes self-generated layouts and scenarios for simulations, but does not provide specific access information (link, DOI, formal citation) to a publicly available or open dataset used for training, nor does it refer to established benchmark datasets. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | Yes | Our experiments are run on machines with Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz, 251GB memory, and NVIDIA A100-SXM4-40GB GPU. |
| Software Dependencies | Yes | We use Python 3.9.7, PyTorch 1.13.1, and CUDA 11.7. |
| Experiment Setup | Yes | For both MAP-Elites and DSAGE, we set b = 50, Neval = 10, 000, T = 1, 000, and Ne = 5. ... We run the lifelong MAPF simulator on every human-designed or optimized layout that we evaluate in this section with Teval = 5, 000 timesteps for, unless explicitly stated otherwise, 10 times... |