An Algorithmic Perspective on Imitation Learning
It addresses the problem of manually programming complex behaviors for robots and intelligent agents by learning from demonstrations, but it is incremental as it serves as a tutorial rather than presenting new research.
This paper provides an introduction to imitation learning, covering its assumptions, algorithms, and implementation tools, with the goal of educating both machine learning experts and roboticists about its challenges and frameworks.
As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning.