In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

A key skill for mobile robots is the ability to navigate efficiently through their environment. In
the case of social or assistive robots, this involves navigating through human crowds …

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a
robot's behavior in novel situations. And since a robot's behavior is often a direct result of its …

One of the most interesting application scenarios in anomaly detection is when sequential
data are targeted. For example, in a safety-critical environment, it is crucial to have an …

Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …