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..
Object-Oriented Dynamics Predictor
Authors: Guangxiang Zhu, Zhiao Huang, Chongjie Zhang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that OODP significantly outperforms previous methods in terms of generalization over novel environments with various object layouts. |
| Researcher Affiliation | Academia | Guangxiang Zhu, Zhiao Huang, and Chongjie Zhang Institute for Interdisciplinary Information Sciences Tsinghua University, Beijing, China EMAIL,EMAIL,EMAIL |
| Pseudocode | No | The paper describes the methodology with figures and text, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of OODP is available online at https://github.com/mig-zh/OODP. |
| Open Datasets | Yes | We evaluate our model on Monster Kong from the Pygame Learning Environment [36], which offers various scenes for testing generalization abilities across object layouts (e.g., different number and spatial arrangement of objects). |
| Dataset Splits | No | The paper describes using 'k training environments' and 'm unseen testing environments' for zero-shot generalization problems but does not specify training/validation/test splits or a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper describes the model architecture and losses but does not provide specific experimental setup details like hyperparameter values (e.g., learning rate, batch size, epochs) or optimizer settings. |