EAN: 9783031004421

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples
provided by a human teacher. The field of LfD has grown into an extensive body of literature
over the past 30 years  with a wide variety of approaches for encoding human demonstrations and
modeling skills and tasks. Additionally  we have recently seen a focus on gathering data from
non-expert human teachers (i.e.  domain experts but not robotics experts). In this book  we
provide an introduction to the field with a focus on the unique technical challenges associated
with designing robots that learn from naive human teachers. We begin  in the introduction  with
a unification of the various terminology seen in the literature as well as an outline of the
design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the
psychology literature that provides insights from human social learning that are relevant to
designing robotic social learners. Chapter 3 walks through an LfD interaction  surveying the
design choices one makes and state of the art approaches in prior work. First  is the choice of
input  how the human teacher interacts with the robot to provide demonstrations. Next  is the
choice of modeling technique. Currently  there is a dichotomy in the field between approaches
that model low-level motor skills and those that model high-level tasks composed of primitive
actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active
learning approaches that allow the robot to refine an existing task model. And finally  Chapter
8 provides best practices for evaluation of LfD systems  with a focus on how to approach
experiments with human subjects in this domain.

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