Learning physics-based locomotion skills is a difficult problem, leading to solutions that
typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of …

We present a real-time character control mechanism using a novel neural network
architecture called a Phase-Functioned Neural Network. In this network structure, the …

The use of deep reinforcement learning allows for high-dimensional state descriptors, but
little is known about how the choice of action representation impacts learning and the …

As for biological mechanisms, which provide a specific functional behavior, the kinematic
synthesis is not so simply applicable without deep considerations on requirements, such as …

While physics-based models for passive phenomena such as cloth and fluids have been
widely adopted in computer animation, physics-based character simulation remains a …

Bipedal robotics is a relatively new research area which is concerned with creating walking
robots which have mobility and agility characteristics approaching those of humans. Also, in …

The aim of this doctoral thesis is to present a body of work aimed at reducing the time spent
by animators manually constructing keyframed animation. To this end we present a number …