Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

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 …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

We develop BatchBALD, a tractable approximation to the mutual information between a
batch of points and model parameters, which we use as an acquisition function to select …

Easy-to-use collaborative robotics solutions, where human workers and robots share their
skills, are entering the market, thus becoming the new frontier in industrial robotics. They …

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

We propose a self-supervised approach for learning representations and robotic behaviors
entirely from unlabeled videos recorded from multiple viewpoints, and study how this …

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 …

In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …

This paper proposes an enhanced robot skill learning system considering both motion
generation and trajectory tracking. During robot learning demonstrations, dynamic …