This project implements adversarial skill embeddings (ASE) for training reusable controllers using reinforcement learning. The system uses Isaac Gym for physics simulation and trains neural network policies to imitate motion datasets and respond to perturbations. The codebase includes pre-training and fine-tuning pipelines for character control tasks like getting up after falling.