Regression Planning Networks
Authors: Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li F. Fei-Fei
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the capabilities of RPN in a grid world environment and a simulated 3D kitchen environment featuring complex visual scene and long task horizon, and show that it achieves near-optimal performance in completely new task instances. |
| Researcher Affiliation | Academia | Danfei Xu Stanford University Roberto Martín-Martín Stanford University De-An Huang Stanford University Yuke Zhu Stanford University Silvio Savarese Stanford University Li Fei-Fei Stanford University |
| Pseudocode | Yes | Algorithm 1 SUBGOALSERIALIZATION |
| Open Source Code | No | The paper does not provide a direct link to the source code for the described methodology or state that it is publicly available in supplementary materials or elsewhere. |
| Open Datasets | No | The paper mentions environments like 'Grid World' and 'Kitchen 3D' and describes training tasks (e.g., 'training tasks are to cook randomly chosen I = 3 ingredients into D = 2 dishes') but does not provide concrete access information (link, DOI, citation with authors/year for a public dataset) for these environments or the data generated within them. |
| Dataset Splits | No | The paper mentions 'training tasks' and 'evaluation tasks' but does not specify the dataset splits (e.g., percentages or sample counts) for train, validation, or test sets. It implies a split by referring to 'training tasks' and 'evaluation tasks' but lacks specific details for reproducibility. |
| Hardware Specification | No | The paper states that the Kitchen 3D environment is 'simulated with [34]' but does not provide any specific hardware details (e.g., GPU models, CPU types, memory) used for running the simulations or training the models. |
| Software Dependencies | No | The paper mentions 'pybullet, a python module for physics simulation, games, robotics and machine learning. http://pybullet.org/, 2016 2017' as a reference, but it does not explicitly state the version numbers of any other software dependencies like Python, PyTorch, TensorFlow, or other libraries used in the implementation. |
| Experiment Setup | No | The paper mentions 'More details on network architectures and evaluation setup are available in the Appendix' and 'More details is included in the Appendix' but does not provide specific hyperparameters or system-level training settings within the main text. |