We introduce RoboMD a deep reinforcement learning-based framework designed to identify failure modes in robotic manipulation policies. By simulating diverse conditions and quantifying failure probabilities, RoboFail provides insights into model robustness and adaptability.

Ensure you have the following dependencies installed:

• Python 3.8+
• CUDA (if using GPU)
• Conda (recommended for managing environments)

1. Clone the repository:

   git clone https://github.com/Robo-MD/Robo-MD-RSS.github.io.git
   cd Robo-MD-RSS.github.io

2. Create a Conda environment:

   conda create --name robomd python=3.8 -y
   conda activate robomd

pip install robosuite

git clone https://github.com/ARISE-Initiative/robomimic.git
cd robomimic
pip install -e .

Install required Python packages:

pip install -r requirements.txt

├── configs/               # Configuration files for actions and training
├── env/                   # Environment implementations
├── scripts/               
├── utils/                 # Utility functions (e.g., loss computations)
├── train_continuous.py     # Training script for continuous latent actions
├── train_discrete.py       # Training script for discrete latent actions
├── train_embedding.py      # Training script for embedding learning
├── README.md               # Project documentation
├── requirements.txt        # Required dependencies

To train an RL policy using a latent action space, run:

python train_continuous.py --name <run_name> --task <task_name> --agent <path_to_agent> --rl_timesteps 3000

Example:

python train_continuous.py --name latent_rl --task lift --agent models/bc_agent.pth --rl_timesteps 50000

For training RL with a discrete action space:

python train_discrete.py --name <run_name> --task <task_name> --agent <path_to_agent> --rl_timesteps 3000

To train and store known embeddings:

python train_embedding.py --path <dataset_path>

This script extracts embeddings from a dataset and stores them in an HDF5 file.

MIT License © 2024 RoboMD Team