Cycle-of-Learning for Autonomous Systems from Human Interaction

Understand

We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms.

• We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning.
• Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them.