Update README.md

README.md

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-license: cdla-permissive-2.0
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+---
+license: cdla-permissive-2.0
+---
+# MoCapAct Dataset
+Control of simulated humanoid characters is a challenging benchmark for sequential decision-making methods, as it assesses a policy’s ability to drive an inherently unstable, discontinuous, and high-dimensional physical system. Motion capture (MoCap) data can be very helpful in learning sophisticated locomotion policies by teaching a humanoid agent low-level skills (e.g., standing, walking, and running) that can then be used to generate high-level behaviors. However, even with MoCap data, controlling simulated humanoids remains very hard, because this data offers only kinematic information. Finding physical control inputs to realize the MoCap-demonstrated motions has required methods like reinforcement learning that need large amounts of compute, which has effectively served as a barrier to entry for this exciting research direction.
+
+In an effort to broaden participation and facilitate evaluation of ideas in humanoid locomotion research, we are releasing MoCapAct (Motion Capture with Actions), a library of high-quality pre-trained agents that can track over three hours of MoCap data for a simulated humanoid in the `dm_control` physics-based environment and rollouts from these experts containing proprioceptive observations and actions. MoCapAct allows researchers to sidestep the computationally intensive task of training low-level control policies from MoCap data and instead use MoCapAct's expert agents and demonstrations for learning advanced locomotion behaviors. It also allows improving on our low-level policies by using them and their demonstration data as a starting point.
+
+In our work, we use MoCapAct to train a single hierarchical policy capable of tracking the entire MoCap dataset within `dm_control`.
+We then re-use the learned low-level component to efficiently learn other high-level tasks.
+Finally, we use MoCapAct to train an autoregressive GPT model and show that it can perform natural motion completion given a motion prompt.
+We encourage the reader to visit our [project website](https://microsoft.github.io/MoCapAct/) to see videos of our results as well as get links to our paper and code.
+
+## File Structure
+
+The file structure of the dataset is:
+```
+├── all
+│   ├── large
+│   │   ├── large_1.tar.gz
+│   │   ├── large_2.tar.gz
+|   │   ...
+│   │   └── large_43.tar.gz
+│   └── small
+│       ├── small_1.tar.gz
+│       ├── small_2.tar.gz
+│       └── small_3.tar.gz
+│
+├── sample 
+│   ├── large.tar.gz
+│   └── small.tar.gz
+│
+└── videos
+    ├── full_clip_videos.tar.gz
+    └── snippet_videos.tar.gz
+```
+
+## MoCapAct Dataset Tarball Files
+The dataset tarball files have the following structure:
+- `all/small/small_*.tar.gz`: Contains HDF5 files with 20 rollouts per snippet. Due to file size limitations, we split the rollouts among multiple tarball files.
+- `all/large/large_*.tar.gz`: Contains HDF5 files with 200 rollouts per snippet. Due to file size limitations, we split the rollouts among multiple tarball files.
+- `sample/small.tar.gz`: Contains example HDF5 files with 20 rollouts per snippet.
+- `sample/large.tar.gz`: Contains example HDF5 files with 200 rollouts per snippet. 
+
+The HDF5 structure is detailed in Appendix A.2 of the paper as well as https://github.com/microsoft/MoCapAct#description.
+
+An example for loading and inspecting an HDF5 file in Python is:
+```python
+import h5py
+dset = h5py.File("/path/to/small/CMU_083_33.hdf5", "r")
+print("Expert actions from first rollout episode:")
+print(dset["CMU_083_33-0-194/0/actions"][...])
+```
+
+## MoCap Videos
+There are two tarball files containing videos of the MoCap clips in the dataset:
+- `full_clip_videos.tar.gz` contains videos of the full MoCap clips.
+- `snippet_videos.tar.gz` contains videos of the snippets that were used to train the experts.
+Note that they are playbacks of the clips themselves, not rollouts of the corresponding experts.