Adds instructions and details to the README

README.md

@@ -1,3 +1,106 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- robotics
+---
+
+# Colosseum Dataset Card
+
+This dataset contains demonstrations for training and testing Imitation Learning
+based policies, taken from our simulation benchmark [`Colosseum`][0], which is
+based on `RLBench`. The benchmark consists of 20 tasks from the RLBench suite.
+We implement [variations][1] for each task, like `camera pose`, which try to
+test generalization capabilities.
+
+## Dataset details
+
+The **training set** consits of 100 demonstrations of the 20 tasks without any
+variation factor (the vanilla version of the RLBench tasks). Each demonstration
+consists of frame data from the following 4 camera views:
+
+- Front camera
+- Left shoulder camera
+- Right shoulder camera
+- Wrist camera
+
+![img-camera-views](./media/colosseum-camera-views.png)
+
+For each camera view we collect the following data:
+
+- RGB
+- Depth
+
+**Note**: each frame is recorded at `128 x 128` resolution.
+
+The **test set** consits of 25 demonstrations of the 20 tasks, each for the
+factors of variations that are applicable to that task. Each step collects data
+from the same 4 camera views, at the same resolution.
+
+## Dataset structure
+
+The data is distributed as `tar.gz` files. After downloading each tar and
+extracting it into a local folder, you'll get a folder structure like the
+following (e.g. for the task `stack_cups`):
+
+![img-folder-structure-1](./media/colosseum-folder-structure-1.png)
+
+Each folder contains a suffix (`idx`), which indicates which variation factor
+was applied to the simulation, e.g. `idx=0`  means **no variations**, whereas
+`idx=2` means **Object Color variation applied to the Manipulated Object**. You
+can find a spreadsheet [here][2] with the tasks `idx` for each of the 20 tasks.
+You can also find what variations are applicable to that task, as it could be
+that some variations are not active for some task combination.
+
+The pickle file `variation_description.pkl` contains the language instructions
+for that task. Below we go deeper into the folder structure for one of the
+variations. Notice there is a set of folders per each episode/demonstration, and
+on each folder there are extra folders for each camera view and type of image.
+There's also a pickle file `low_dim_obs.pkl` with the low dimensional observation
+saved by RLBench. The info stored in this pickle comes from [this][3] config
+file in RLBench.
+
+![img-folder-structure-2](./media/colosseum-folder-structure-2.png)
+
+## Downloading the dataset using wget and a download link
+
+1. Go to the HuggingFace repo and select the files option:
+
+![img-hf-files](./media/img_huggingface_files_section.png)
+
+2. Select the task you want to get:
+
+![img-hf-task](./media/img_huggingface_task_selection.png)
+
+3. Get the download link:
+
+![img-hf-download-link](./media/img_huggingface_download_link.png)
+
+4. Use `curl` or `wget` to get the tar file:
+
+```bash
+wget YOUR_DOWNLOAD_LINK
+```
+
+
+## Resources for more information
+
+- Paper: https://arxiv.org/abs/2402.08191
+- Benchmark Code: https://github.com/robot-colosseum/robot-colosseum
+- Website: https://robot-colosseum.github.io
+
+## Citation
+
+If you find our work helpful, please consider citing our paper.
+```
+@article{pumacay2024colosseum,
+  title     = {THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation}, 
+  author    = {Pumacay, Wilbert and Singh, Ishika and Duan, Jiafei and Krishna, Ranjay and Thomason, Jesse and Fox, Dieter},
+  booktitle = {arXiv preprint arXiv:2402.08191},
+  year      = {2024},
+}
+```
+
+[0]: <https://robot-colosseum.readthedocs.io/en/latest/overview.html> (colosseum-overview)
+[1]: <https://robot-colosseum.readthedocs.io/en/latest/overview.html#perturbations> (colosseum-perturbations)
+[2]: <https://docs.google.com/spreadsheets/d/175cCG9qHzNB6axSno6K2NjQ9gjpbCqNK9GCi-SAQkCM/edit?usp=sharing> (colosseum-tasks-distribution)
+[3]: <https://github.com/MohitShridhar/RLBench/blob/ad991951bc53e4f3b73b803a75cf4b7d55295cf7/rlbench/observation_config.py#L73> (rlbench-task-lowdim)