initial commit

.gitmodules

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+[submodule "oscar/rl_games"]
+	path = oscar/rl_games
+	url = https://github.com/cremebrule/rl_games.git
+	branch = oscar

LICENSE.txt

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+Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+NVIDIA CORPORATION and its licensors retain all intellectual property
+and proprietary rights in and to this software, related documentation
+and any modifications thereto.  Any use, reproduction, disclosure or
+distribution of this software and related documentation without an express
+license agreement from NVIDIA CORPORATION is strictly prohibited.

README.md

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+## OSCAR
+This repository contains the codebase used in [OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation](arxiv_link_TODO).
+
+More generally, this codebase is a modular framework built upon [IsaacGym](https://developer.nvidia.com/isaac-gym), and intended to support future robotics research leveraging large-scale training.
+
+Of note, this repo contains:
+- High-quality controller implementations of OSC, IK, and Joint-Based controllers that have been fully parallelized for PyTorch
+- Complex Robot Manipulation tasks for benchmarking learning algorithms
+- Modular structure enabling rapid prototyping of additional robots, controllers, and environments
+
+## Requirements
+- Linux machine
+- Conda
+- NVIDIA GPU + CUDA
+
+## Getting Started
+First, clone this repo:
+
+```bash
+git clone https://github.com/NVlabs/oscar.git
+```
+
+Next, create a new conda environment to be used for this repo and activate the repo:
+```bash
+cd oscar
+bash create_conda_env_oscar.sh
+conda activate oscar
+```
+
+This will create a new conda environment named `oscar` and additional install some dependencies. Next, we need IsaacGym. This repo itself does not contain IsaacGym, but is compatible with any version > preview 3.0.
+
+Install and build IsaacGym [HERE](https://developer.nvidia.com/isaac-gym).
+
+Once installed, navigate to the python directory and install the package to this conda environment:
+
+```bash
+cd <ISAACGYM_REPO_PATH>/python
+pip install -e .
+```
+
+Now with IsaacGym installed, we can finally install this repo as a package:
+
+```bash
+cd <OSCAR_REPO_PATH>
+pip install -e .
+```
+
+That's it!
+
+## Prerequisites
+
+- Supported platforms are Linux (Ubuntu 16.04+) and Windows 10 (version 1809 is required).
+
+- Install Git and [Git LFS](https://github.com/git-lfs/git-lfs/wiki/Installation).
+
+- If you wish to develop using Isaac Gym, you should [fork this repository](https://gitlab-master.nvidia.com/carbon-gym/carbgym/forks/new).
+  * Go to your newly created fork in GitLab, select
+      * go to "Settings->Repository->Mirroring repositories"
+          * set "Git repository URL" to https://gitlab-master.nvidia.com/carbon-gym/carbgym.git
+          * select "Pull" under "Mirror direction".
+          * clear out the text under "Password".
+          * check the "Overwrite diverged branches" checkbox.
+      * go to "Settings->General->Visibility, project features, permissions"
+          * ensure "Project Visibility" is set to "Public".
+  * Clone your fork to a local hard drive.  On Windows, make sure to use a NTFS drive.
+
+- If you just want to use Isaac Gym as-is, you can clone this repositiory directly to your hard drive (on Windows, make sure to use a NTFS drive).
+
+- On Ubuntu, you should run `./setup.sh` to install some prerequisite software.  This only needs to be done once. 
+
+
+## Building
+
+Execute `./build.sh` on Linux or `build.bat` on Windows.
+
+For building in Visual Studio you need VS 2017 with [SDK 10.17763+](https://go.microsoft.com/fwlink/?LinkID=2023014).
+
+The build output will be found in the generated `_build` folder and the make/solution 
+files will be found in the generated `_compiler` directory. 
+
+## Python
+
+See the [Python README](/python/README.md) for information on running the Python examples.
+
+## Contributing
+
+Please read [CONTRIBUTING.md](CONTRIBUTING.md).

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