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Robot Learning from Human Demonstration (Paperback)
Loot Price: R1,081
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Robot Learning from Human Demonstration (Paperback)
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
Expected to ship within 10 - 15 working days
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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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