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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
Authors: Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala, Marielle Stoelinga9669-9678
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,039 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,365 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Realistic benchmarks show the practical applicability of our method, even when the i MDP has millions of states or transitions. We implement our iterative abstraction method in Python, and tailor the model checker PRISM (Kwiatkowska, Norman, and Parker 2011) for i MDPs to compute robust optimal policies. ... We report the performance of our method on: (1) a UAV motion control, (2) a building temperature regulation, and (3) a spacecraft rendezvous problem. |
| Researcher Affiliation | Academia | 1 Radboud University, Nijmegen, the Netherlands 2 University of Oxford, Oxford, United Kingdom 3 University of Birmingham, Birmingham, United Kingdom 4 University of Kentucky, Kentucky, USA 5 University of Twente, Enschede, the Netherlands |
| Pseudocode | Yes | Algorithm 1: Sampling-based i MDP abstraction. |
| Open Source Code | Yes | Our codes are available via https://gitlab.science.ru.nl/tbadings/sample-abstract |
| Open Datasets | No | The paper uses generated noise samples from a simulator and discusses problem benchmarks from other papers (e.g., Cauchi and Abate (2018), Vinod, Gleason, and Oishi (2019)), but it does not provide concrete access information (link, DOI, repository, or specific citation for a dataset) for any publicly available or open dataset used in its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | all experiments are run on a computer with 32 3.7GHz cores and 64 GB of RAM. |
| Software Dependencies | No | The paper states 'We implement our iterative abstraction method in Python, and tailor the model checker PRISM (Kwiatkowska, Norman, and Parker 2011) for i MDPs to compute robust optimal policies,' but it does not provide specific version numbers for Python, PRISM, or any other libraries. |
| Experiment Setup | Yes | In all benchmarks, we use Theorem 1 with β = 0.01, and apply the iterative scheme with γ = 2, starting at N = 25, with an upper bound of 12, 800 samples. |