Continuous action policy search is currently the focus of intensive research, driven both by
the recent success of deep reinforcement learning algorithms and the emergence of …

This article approaches the problem of controlling quadrupedal running and jum**
motions with a parameterized, model-based, state-feedback controller. Inspired by the motor …

This chapter presents an overview of learning approaches for the acquisition of controllers
and movement skills in humanoid robots. The term learning control refers to the process of …

Augmenting reinforcement learning with imitation learning is often hailed as a method by
which to improve upon learning from scratch. However, most existing methods for integrating …

Reinforcement learning has become more and more popular in robotics for acquiring
feedback controllers. Many approaches aim for learning a controller from scratch, ie, data …

In recent years, attention has been focused on the relationship between black-box
optimization problem and reinforcement learning problem. In this research, we propose the …

Methods of optimizing a single trajectory are mature enough for planning in many
applications. Yet such optimization methods applied to high Degree-Of-Freedom robots …

Usually, real-world problems involve the optimization of multiple, possibly conflicting,
objectives. These problems may be addressed by Multi-objective Reinforcement learning …

In the recent past, the wide availability of digital technologies has strongly disrupted many
well-established manufacturing techniques and initiated a rapid transformation process in …

Abstract Machines have facilitated our lives since centuries. The next generation of mobile
helpers may have the abilities and agility of legged animals and can thus support us in …