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In this book, we have set up a unified analytical framework for
various human-robot systems, which involve peer-peer interactions
(either space-sharing or time-sharing) or hierarchical
interactions. A methodology in designing the robot behavior through
control, planning, decision and learning is proposed. In
particular, the following topics are discussed in-depth: safety
during human-robot interactions, efficiency in real-time robot
motion planning, imitation of human behaviors from demonstration,
dexterity of robots to adapt to different environments and tasks,
cooperation among robots and humans with conflict resolution. These
methods are applied in various scenarios, such as human-robot
collaborative assembly, robot skill learning from human
demonstration, interaction between autonomous and human-driven
vehicles, etc. Key Features: Proposes a unified framework to model
and analyze human-robot interactions under different modes of
interactions. Systematically discusses the control, decision and
learning algorithms to enable robots to interact safely with humans
in a variety of applications. Presents numerous experimental
studies with both industrial collaborative robot arms and
autonomous vehicles.
In this book, we have set up a unified analytical framework for
various human-robot systems, which involve peer-peer interactions
(either space-sharing or time-sharing) or hierarchical
interactions. A methodology in designing the robot behavior through
control, planning, decision and learning is proposed. In
particular, the following topics are discussed in-depth: safety
during human-robot interactions, efficiency in real-time robot
motion planning, imitation of human behaviors from demonstration,
dexterity of robots to adapt to different environments and tasks,
cooperation among robots and humans with conflict resolution. These
methods are applied in various scenarios, such as human-robot
collaborative assembly, robot skill learning from human
demonstration, interaction between autonomous and human-driven
vehicles, etc. Key Features: Proposes a unified framework to model
and analyze human-robot interactions under different modes of
interactions. Systematically discusses the control, decision and
learning algorithms to enable robots to interact safely with humans
in a variety of applications. Presents numerous experimental
studies with both industrial collaborative robot arms and
autonomous vehicles.
In real-world traffic, rational human drivers can make
socially-compatible decisions in complex and crowded scenarios by
efficiently negotiating with their surroundings using
non-linguistic communications such as gesturing, deictics, and
motion cues. Understanding the principles and rules of the dynamic
interaction among human drivers in complex traffic scenes allows 1)
generating diverse social driving behaviors that leverage beliefs
and expectations about others' actions or reactions; 2) predicting
the future states of a scene with moving objects, which is
essential to building probably safe intelligent vehicles with the
capabilities of behavior prediction and potential collision
detection; and 3) creating realistic driving simulators. However,
this task is not trivial since various social factors exist along
the driving interaction process, including social motivation,
social perception, and social control. Generally, human driving
behavior is compounded by human drivers' social interactions and
their physical interactions with the scene. No human drives a car
in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can
interact with other road users in a socially-compatible way through
implicit communications to complete their driving tasks smoothly in
interaction-intensive, safety-critical environments. This monograph
reviews the existing approaches and theories to help understand and
rethink the interactions among human drivers toward social
autonomous driving. Fundamental questions which are covered
include: 1) What is social interaction in road traffic scenes? 2)
How to measure and evaluate social interaction? 3) How to model and
reveal the process of social interaction? 4) How do human drivers
reach an implicit agreement and negotiate smoothly in social
interaction? This monograph reviews various approaches to modeling
and learning the social interactions between human drivers, ranging
from optimization theory, deep learning, and graphical models to
social force theory and behavioral and cognitive science. Also
highlighted are some new directions, critical challenges, and
opening questions for future research.
Neural networks have been widely used in many applications, such as
image classification and understanding, language processing, and
control of autonomous systems. These networks work by mapping
inputs to outputs through a sequence of layers. At each layer, the
input to that layer undergoes an affine transformation followed by
a simple nonlinear transformation before being passed to the next
layer. Neural networks are being used for increasingly important
tasks, and in some cases, incorrect outputs can lead to costly
consequences, hence validation of correctness at each layer is
vital. The sheer size of the networks makes this not feasible using
traditional methods. In this monograph, the authors survey a class
of methods that are capable of formally verifying properties of
deep neural networks. In doing so, they introduce a unified
mathematical framework for verifying neural networks, classify
existing methods under this framework, provide pedagogical
implementations of existing methods, and compare those methods on a
set of benchmark problems. Algorithms for Verifying Deep Neural
Networks serves as a tutorial for students and professionals
interested in this emerging field as well as a benchmark to
facilitate the design of new verification algorithms.
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