We present a method for precomputing robust task-based control policies for physically
simulated characters. This allows for characters that can demonstrate skill and purpose in …

Simulated characters in simulated worlds require simulated skills. We develop control
strategies that enable physically-simulated characters to dynamically navigate environments …

We introduce a method for learning low-dimensional linear feedback strategies for the
control of physics-based animated characters around a given reference trajectory. This …

The goal of this research is to create physically simulated biped characters equipped with a
rich repertoire of motor skills. The user can control the characters interactively by modulating …

The locomotion skills developed for physics-based characters most often target flat terrain.
However, much of their potential lies with the creation of dynamic, momentum-based …

Human characters with a broad range of natural looking and physically realistic behaviors
will enable the construction of compelling interactive experiences. In this paper, we develop …

Learning locomotion skills is a challenging problem. To generate realistic and smooth
locomotion, existing methods use motion capture, finite state machines or morphology …

In this paper we show how the task of motion tracking for physically simulated characters
can be solved using supervised learning and optimizing a policy directly via back …

Dynamically simulated characters are difficult to control because they are underactuated---
they have no direct control over their global position and orientation. In order to succeed …

The difficulty of developing control strategies has been a primary bottleneck in the adoption
of physics-based simulations of human motion. We present a method for learning robust …