Robust Visual Imitation Learning with Inverse Dynamics Representations
Authors: Siyuan Li, Xun Wang, Rongchang Zuo, Kewu Sun, Lingfei Cui, Jishiyu Ding, Peng Liu, Zhe Ma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the proposed approach under various visual perturbations and in diverse visual control tasks. |
| Researcher Affiliation | Collaboration | Siyuan Li1*, Xun Wang2*, Rongchang Zuo1, Kewu Sun2, Lingfei Cui3, Jishiyu Ding2, Peng Liu1, Zhe Ma2 1Harbin Institute of Technology 2Intelligent Science & Technology Academy Limited of CASIC 3Institute of Computer Application Technology, Norinco Group |
| Pseudocode | Yes | In Appendix A, we provide the pseudocode and the algorithmic details of RILIR. |
| Open Source Code | Yes | The code to reproduce these results is available in the supplementary material. |
| Open Datasets | Yes | We conduct extensive experiments on a set of visual control tasks in Meta-World domain (Yu et al. 2020) and Deep Mind Control Suite (DMC) (Tassa et al. 2018). |
| Dataset Splits | No | The paper mentions using well-known control suites like Meta-World and DMC, but it does not provide specific details on how the data from these suites are split into training, validation, and test sets, either by percentages, sample counts, or references to predefined splits. |
| Hardware Specification | Yes | These experiments have been run with A100 GPUs, and each run takes no more than 1 day. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In Appendix C, we provide the hyperparameters for all the baselines and the proposed approach. |