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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics
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Interactive Imitation Learning in Robotics - A Survey (Paperback)
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Interactive Imitation Learning in Robotics - A Survey (Paperback)
Series: Foundations and Trends (R) in Robotics
Expected to ship within 10 - 15 working days
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Existing robotics technology is still mostly limited to being used
by expert programmers who can adapt the systems to new required
conditions, but not flexible and adaptable by non-expert workers or
end-users. Imitation Learning (IL) has obtained considerable
attention as a potential direction for enabling all kinds of users
to easily program the behavior of robots or virtual agents.
Interactive Imitation Learning (IIL) is a branch of Imitation
Learning (IL) where human feedback is provided intermittently
during robot execution allowing an online improvement of the
robot's behavior. In this monograph, research in IIL is presented
and low entry barriers for new practitioners are facilitated by
providing a survey of the field that unifies and structures it. In
addition, awareness of its potential is raised, what has been
accomplished and what are still open research questions being
covered. Highlighted are the most relevant works in IIL in terms of
human-robot interaction (i.e., types of feedback), interfaces
(i.e., means of providing feedback), learning (i.e., models learned
from feedback and function approximators), user experience (i.e.,
human perception about the learning process), applications, and
benchmarks. Furthermore, similarities and differences between IIL
and Reinforcement Learning (RL) are analyzed, providing a
discussion on how the concepts offline, online, off-policy and
on-policy learning should be transferred to IIL from the RL
literature. Particular focus is given to robotic applications in
the real world and their implications are discussed, and
limitations and promising future areas of research are provided.
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