Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in
higher dimensions. Simulation can aid in optimizing these controllers if parameters learned …

In the past decade, numerous machine learning algorithms have been shown to
successfully learn optimal policies to control real robotic systems. However, it is common to …

Learning for control can acquire controllers for novel robotic tasks, paving the path for
autonomous agents. Such controllers can be expert-designed policies, which typically …

The most data-efficient algorithms for reinforcement learning in robotics are model-based
policy search algorithms, which alternate between learning a dynamical model of the robot …

Model predictive control (MPC) has shown great success for controlling complex systems,
such as legged robots. However, when closing the loop, the performance and feasibility of …

Sample efficiency is important when optimizing parameters of locomotion controllers, since
hardware experiments are time consuming and expensive. Bayesian Optimization, a sample …

Properly designing a system to exhibit favorable natural dynamics can greatly simplify
designing or learning the control policy. However, it is still unclear what constitutes favorable …

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.
Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct …

High-speed and high-acceleration movements are inherently hard to control. Applying
learning to the control of such motions on anthropomorphic robot arms can improve the …