This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

Abstract Representation learning algorithms are designed to learn abstract features that
characterize data. State representation learning (SRL) focuses on a particular kind of …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Imitation learning has been studied widely as a convenient way to transfer human skills to
robots. This learning approach is aimed at extracting relevant motion patterns from human …

Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two
popular approaches to policy search. The former is widely applicable and rather stable, but …

Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

We describe the winning entry to the Amazon Picking Challenge. From the experience of
building this system and competing in the Amazon Picking Challenge, we derive several …

Robot learning through kinesthetic teaching is a promising way of cloning human behaviors,
but it has its limits in the performance of complex tasks with small amounts of data, due to …

In continuous action domains, standard deep reinforcement learning algorithms like DDPG
suffer from inefficient exploration when facing sparse or deceptive reward problems …

We consider an imitation learning approach to model robot point-to-point (also known as
discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each …