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 …

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 …

ROBOTI LLC AND DEEPMIND TECHNOLOGIES LIMITED (COLLECTIVELY, LICENSORS) SPECIFICALLY DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT …

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 …

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 …

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.

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

The models have been designed and fine-tuned by Roboti LLC based on information, mesh files and feedback provided by the device manufacturers, as well as testing and feedback from …

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 …

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