Simulations are attractive environments for training agents as they provide an abundant
source of data and alleviate certain safety concerns during the training process. But the …

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 …

Modeling human motor control and predicting how humans will move in novel environments
is a grand scientific challenge. Researchers in the fields of biomechanics and motor control …

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 enabling …

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 …

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

Data-driven character animation based on motion capture can produce highly naturalistic
behaviors and, when combined with physics simulation, can provide for natural procedural …

Reinforcement learning offers a promising methodology for developing skills for simulated
characters, but typically requires working with sparse hand-crafted features. Building on …

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that
uses standard supervised learning methods as subroutines. Our goal is an algorithm that …

Developing robust walking controllers for bipedal robots is a challenging endeavor.
Traditional model-based locomotion controllers require simplifying assumptions and careful …