Cross-domain Imitation from Observations
Authors: Dripta S. Raychaudhuri, Sujoy Paul, Jeroen Vanbaar, Amit K. Roy-Chowdhury
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across a wide variety of challenging domains demonstrate the efficacy of our approach. |
| Researcher Affiliation | Collaboration | 1University of California, Riverside 2Google Research 3Mitsubishi Electric Research Laboratories |
| Pseudocode | Yes | Algorithm 1 Learn domain transformation ψ |
| Open Source Code | Yes | Code and videos are available at: https://driptarc.github.io/xdio.html. |
| Open Datasets | No | The paper describes generating trajectories within environments like MuJoCo and OpenAI Gym, but it does not provide a specific link, DOI, repository, or formal citation for a pre-collected, publicly available dataset used for training. The data is generated dynamically. |
| Dataset Splits | No | The paper does not explicitly state specific percentages or counts for training, validation, and test splits. It mentions collecting 'a dataset of state-action triplets P = {(st A, at A, st+1 A )} by random exploration' but does not specify how this or other data is split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. It only mentions using the MuJoCo physics engine. |
| Software Dependencies | No | The paper mentions software like MuJoCo and Open AI Gym, and algorithms like PPO and A3C, but does not specify version numbers for any of these components, which is required for reproducible software dependency information. |
| Experiment Setup | No | The paper states, 'Implementation details are presented in the supplementary materials.' This indicates that specific experimental setup details, such as hyperparameters or training configurations, are not provided in the main text of the paper. |