Object-Oriented Dynamics Predictor
Authors: Guangxiang Zhu, Zhiao Huang, Chongjie Zhang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 guangxiangzhu@outlook.com,hza14@mails.tsinghua.edu.cn,chongjie@tsinghua.edu.cn |
| 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. |