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 …

Here we present deep-learning techniques for healthcare, centering our discussion on deep
learning in computer vision, natural language processing, reinforcement learning, and …

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a mapping …

This review examines the dichotomy between automatic and autonomous behaviors in
surgical robots, maps the possible levels of autonomy of these robots, and describes the …

For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

Cables are complex, high-dimensional, and dynamic objects. Standard approaches to
manipulate them often rely on conservative strategies that involve long series of very slow …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In this paper we tackle the problem of deformable object manipulation through model-free
visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we …

The long short-term memory (LSTM) neural network is capable of processing complex
sequential information since it utilizes special gating schemes for learning representations …

Manipulation of deformable objects, such as ropes and cloth, is an important but challenging
problem in robotics. We present a learning-based system where a robot takes as input a …