It was developed by Roboti LLC and was available as a commercial product from 2015 to 2021. We are excited to announce that as of October 2021, DeepMind has acquired MuJoCo and has made it …

This page contains legacy MuJoCo releases from Roboti LLC (versions 2.0 and earlier) followed by the list of changes. These versions require an activation key. A free license with unlocked activation key …

Preface This is an online book about the MuJoCo physics simulator. It contains all the information needed to use MuJoCo effectively. It includes introductory material, technical explanation of the …

ROBOTI LLC AND DEEPMIND TECHNOLOGIES LIMITED (COLLECTIVELY, LICENSORS) SPECIFICALLY DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT …

This chapter provides a comprehensive XML reference for MuJoCo, detailing syntax and features for creating and customizing physics simulations.

See also citation data from Google Scholar PHD THESES Integration of control and dynamical systems perspectives to machine learning Motoya Ohnishi (2024). University of Washington Broad …

Optico is Emo Todorov's latest project. It is a toolbox for optimization and control built on top of MuJoCo physics. Optico was introduced to the technical community at a NeurIPS 2019 workshop …

Emo Todorov also "graduated" and is now working on research and development at Roboti LLC. This is an archival page, previously hosted at the University of Washington.

Yuval Tassa†, Nicolas Mansard∗ and Emo Todorov† Abstract—Trajectory optimizers are a powerful class of methods for generating goal-directed robot motion. Differential Dynamic Programming (DDP) …

MuJoCo is our physics engine designed for model-based control. It combines recursive algorithms in generalized coordinates, and velocity-stepping using contact impulse solvers. The solvers are based …