Symmetry-Aware Robot Design with Structured Subgroups
Authors: Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further empirically evaluate SARD on various tasks, and the results show its superior efficiency and generalizability. |
| Researcher Affiliation | Academia | 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Huazhong University of Science and Technology 3Harvard University. |
| Pseudocode | Yes | Algorithm 1 SARD: Symmetry-Aware Robot Design |
| Open Source Code | Yes | And our code is available at Git Hub2. |
| Open Datasets | Yes | Environments. All six tasks are adapted from Gupta et al. (2021b), which are created based on Mu Jo Co (Todorov et al., 2012) physics simulator. |
| Dataset Splits | No | The paper describes the reinforcement learning setup and experimental evaluation but does not specify explicit training, validation, and test dataset splits for the environment data. |
| Hardware Specification | Yes | Experiments are carried out on NVIDIA GTX 2080 Ti GPUs. ... SARD requires approximately 10G of RAM and 4G of video memory... |
| Software Dependencies | No | The paper mentions software like Py Torch (Paszke et al., 2019), Py Torch Geometric package (Fey & Lenssen, 2019), and Mu Jo Co (Todorov et al., 2012) with citations, but does not provide explicit version numbers (e.g., PyTorch 1.9, MuJoCo 2.0) for reproducibility. |
| Experiment Setup | Yes | Here we provide the hyperparameters needed to replicate our experiments in Table 1, and we also include our codes in the supplementary. Table 1: Hyperparameters of SARD and Transform2Act. [Lists hyperparameters such as Learning Rate, Batch Size, Discount Factor, etc.] |