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 [1].
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification
Authors: Mingdong Wu, Fangwei Zhong, Yulong Xia, Hao Dong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results in ball rearrangement and room rearrangement demonstrate that our method significantly outperforms the state-of-the-art methods in the quality of the terminal state, the efficiency of the control process, and scalability. |
| Researcher Affiliation | Academia | Mingdong Wu* 1, 3, Fangwei Zhong* 2, 3, Yulong Xia1, Hao Dong1, 4 1 Center on Frontiers of Computing Studies, School of Computer Science, Peking University 2 School of Intelligence Science and Technology, Peking University 3 Beijing Institute for General Artificial Intelligence (BIGAI) 4 Peng Cheng Laboratory EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and demo videos are on https://sites.google.com/view/targf. |
| Open Datasets | Yes | Room Rearrangement is built on a more realistic dataset, 3D-FRONT [46]. |
| Dataset Splits | No | The paper specifies training and testing splits for the Room Rearrangement dataset (756 for training, 83 for testing) but does not explicitly mention a 'validation' split. |
| Hardware Specification | No | The main paper states that hardware details are provided in the supplemental material, but does not include them in the paper itself. |
| Software Dependencies | No | The paper cites external tools like 'Pybullet' and 'Soft actor-critic (sac) implementation in pytorch' but does not list specific version numbers for its own software dependencies. |
| Experiment Setup | No | The main paper states that training details and hyperparameters are provided in the supplemental material, but does not include them in the paper itself. |