

Protomotions
Overview :
ProtoMotions is a project dedicated to creating interactive physics-based virtual agents. It supports IsaacGym and IsaacSim and is built on Hydra and OmegaConfig, facilitating simple configuration management. This project offers researchers and developers a platform to develop and test physics-based character animation techniques, applicable not only to academic research but also in fields such as gaming, film, and virtual reality.
Target Users :
The primary audience includes researchers and developers in the fields of computer graphics, machine learning, and animation. They can utilize ProtoMotions to explore and develop new animation technologies or apply it to their own projects to enhance the realism and interactivity of character animations.
Use Cases
Researchers use ProtoMotions to train a full-body motion tracker to improve motion capture accuracy.
Game developers utilize the AMP model in ProtoMotions to generate natural movements for in-game characters.
Filmmakers leverage ProtoMotions to create fluid character animations in complex scenes.
Features
Supports both IsaacGym and IsaacSim, allowing flexible choice of simulation backends.
Built on Hydra and OmegaConfig for convenient configuration management.
Offers various pre-trained models, such as AMP and ASE, for different animation tasks.
Allows customization of environment and agents for user-specific development needs.
Provides detailed installation and usage instructions for quick onboarding.
Supports multiple robot models, including SMPL, SMPL-X, and AMP.
Includes terrain generation and scene management features to enhance animation realism.
Supports experiment logging with Tensorboard and Weights & Biases.
How to Use
First, make sure to have Python 3.8 and the required dependencies installed.
Next, clone the ProtoMotions GitHub repository to your local machine.
Install IsaacGym or IsaacSim, choosing the simulation backend according to your needs.
Set the PYTHON_PATH environment variable to point to the root directory of ProtoMotions.
Select appropriate configuration files and robot models based on your experimental requirements.
Run the training script to start training the agent.
Once training is complete, use the evaluation script to test the agent's performance.
Feel free to customize the environment and agent as needed for more in-depth experiments.