A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

We investigate whether a robot arm can learn to pick and throw arbitrary rigid objects into
selected boxes quickly and accurately. Throwing has the potential to increase the physical …

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 …

Reinforcement learning provides a powerful and flexible framework for automated
acquisition of robotic motion skills. However, applying reinforcement learning requires a …

Policy search and model predictive control (MPC) are two different paradigms for robot
control: policy search has the strength of automatically learning complex policies using …

Model predictive control (MPC) is an effective method for controlling robotic systems,
particularly autonomous aerial vehicles such as quadcopters. However, application of MPC …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

Policy search is a subfield in reinforcement learning which focuses on finding good
parameters for a given policy parametrization. It is well suited for robotics as it can cope with …

Direct policy search can effectively scale to high-dimensional systems, but complex policies
with hundreds of parameters often present a challenge for such methods, requiring …