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].
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Authors: Haojie Huang, Owen Lewis Howell, Dian Wang, Xupeng Zhu, Robert Platt, Robin Walters
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tests on the RLbench benchmark achieve state-of-the-art results across various tasks. |
| Researcher Affiliation | Academia | Northeastern University, Boston, MA 02115, USA |
| Pseudocode | Yes | Algorithm 1 Four Tran inference |
| Open Source Code | No | The paper provides a 'Project website' link, but it does not explicitly state that the source code for the described methodology is available there, nor does it link directly to a code repository. |
| Open Datasets | Yes | Tests on the RLbench benchmark achieve state-of-the-art results across various tasks. (James et al. (2020)) |
| Dataset Splits | No | The paper mentions evaluating 'the best evaluation across the training process' but does not specify a distinct validation dataset split (e.g., by percentage or count) for reproducibility. |
| Hardware Specification | Yes | Tests were performed on NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions software components like UNet and Adam optimizer, but does not provide specific version numbers for these or other critical software dependencies required for reproduction. |
| Experiment Setup | Yes | We train our method with {1, 5, 10} demonstrations and train the baselines with 10 demonstrations on each task individually. All methods are trained for 15K SGD steps, and we evaluate them on 25 unseen configurations every 5K steps. [...] The model is trained using the Adam optimizer with fixed learning rate=1e 4. |