Interactive Imitation Learning

Interactive imitation learning (IIL) aims to efficiently train robots by combining expert demonstrations with online feedback, overcoming limitations of traditional imitation learning methods. Current research focuses on improving safety and robustness, particularly for complex or safety-critical tasks, through techniques like adversarial training to expose the system to critical states and algorithms that leverage human-perceived precision to guide intervention. This approach offers a data-efficient and adaptable alternative to reinforcement learning, with significant potential for advancing robotics in areas such as industrial automation and human-robot collaboration.