Update README.md

README.md

@@ -6,7 +6,25 @@ license: cc-by-nc-sa-4.0
 
 ## Dataset Summary
 
-Official release of datasets for the MimicGen paper. These robot demonstration datasets were generated automatically using the MimicGen system, using a small number of human teleoperated demos. See [this link](https://github.com/NVlabs/mimicgen_environments#dataset-types) for more information on the dataset structure and how to use them.
+This repository contains the official release of datasets for the [CoRL 2023](https://www.corl2023.org/) paper "MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations".
+
+[**[Website]**](https://mimicgen.github.io)   [**[Paper]**](https://openreview.net/forum?id=dk-2R1f_LR)
+
+## Dataset Structure
+
+Each dataset is an hdf5 file that is readily compatible with [robomimic](https://robomimic.github.io/) --- the structure is explained [here](https://robomimic.github.io/docs/datasets/overview.html#dataset-structure). 
+
+As described in the paper, each task has a default reset distribution (D_0). Source human demonstrations (usually 10 demos) were collected on this distribution and MimicGen was subsequently used to generate large datasets (usually 1000 demos) across different task reset distributions (e.g. D_0, D_1, D_2), objects, and robots.
+
+The datasets are split into different types:
+
+- **source**: source human datasets used to generate all data -- this generally consists of 10 human demonstrations collected on the D_0 variant for each task.
+- **core**: datasets generated with MimicGen for different task reset distributions. These correspond to the core set of results in Figure 4 of the paper.
+- **object**: datasets generated with MimicGen for different objects. These correspond to the results in Appendix G of the paper.
+- **robot**: datasets generated with MimicGen for different robots. These correspond to the results in Appendix F of the paper.
+- **large_interpolation**: datasets generated with MimicGen using much larger interpolation segments. These correspond to the results in Appendix H in the paper.
+
+**Note**: We found that the large_interpolation datasets pose a significant challenge for imitation learning, and have substantial room for improvement.
 
 ## Citation