Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of
a manipulator so that the end effector moves to a desired position; IK can be applied in many …

Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Synthesizing graceful and life-like behaviors for physically simulated characters has been a
fundamental challenge in computer animation. Data-driven methods that leverage motion …

Reproducing the diverse and agile locomotion skills of animals has been a longstanding
challenge in robotics. While manually-designed controllers have been able to emulate many …

A longstanding goal in character animation is to combine data-driven specification of
behavior with a system that can execute a similar behavior in a physical simulation, thus …

This paper is an introductory tutorial for numerical trajectory optimization with a focus on
direct collocation methods. These methods are relatively simple to understand and …

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 …

High-quality motion capture datasets are now publicly available, and researchers have used
them to create kinematics-based controllers that can generate plausible and diverse human …

Training a bipedal character to play basketball and interact with objects, or a quadruped
character to move in various locomotion modes, are difficult tasks due to the fast and …