MUJOCO
MuJoCo is a physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed.
MUJOCO
Industry:
Animation Robotics Search Engine
Website Url:
http://www.mujoco.org
Status:
Active
Technology used in webpage:
Viewport Meta IPhone / Mobile Compatible SPF SSL By Default Apple Mobile Web Clips Icon IPv6 Google Google DNS Google Domains Read The Docs
Similar Organizations
FilesTube.to
FilesTube.com was a search engine designed to search files in various file sharing and uploading sites.
Naver Labs
Naver Labs is Naver Corp's Research and Development arm with focus on AI, robotics, and autonomous driving.
Zoro.to
Zoro.to is a free site to watch anime and you can even download subbed or dubbed anime in ultra HD quality.
Official Site Inspections
http://www.mujoco.org Semrush global rank: 1.81 M Semrush visits lastest month: 12.63 K
- Host name: any-in-2015.1e100.net
- IP address: 216.239.32.21
- Location: Mexico City Mexico
- Latitude: 19.3421
- Longitude: -99.1927
- Timezone: America/Mexico_City
- Postal: 01090

More informations about "MuJoCo"
MuJoCo โ Advanced Physics Simulation
MuJoCo is a free and open source physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast โฆSee details»
GitHub - google-deepmind/mujoco: Multi-Joint dynamics with โฆ
Overview - MuJoCo Documentation - Read the Docs
MuJoCo is a C/C++ library with a C API, intended for researchers and developers. The runtime simulation module is tuned to maximize performance and operates on low-level data โฆSee details»
MuJoCo - Gymnasium Documentation - The Farama Foundation
MuJoCo stands for Multi-Joint dynamics with Contact. It is a physics engine for facilitating research and development in robotics, biomechanics, graphics and animation, and other areas โฆSee details»
MuJoCo - Wikipedia
MuJoCo, short for Multi-Joint dynamics with Contact, is a general purpose physics engine that is tailored to scientific use cases such as robotics, biomechanics and machine learning. It was first described in 2012 in a paper by Emanuel Todorov, Tom Erez, and Yuval Tassa, and later commercialized under Roboti LLC. According to a Google Scholar search, as of April 2024 the original publication has been cited 5329 times, and the MuJoCo engine 9250 times. It was descโฆSee details»
MuJoCo - Crunchbase Company Profile & Funding
MuJoCo is a physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation.See details»
Open-sourcing MuJoCo - Google DeepMind
May 23, 2022ย ยท MuJoCo is one of the few full-featured simulators backed by an established company, which is truly open source. As a research-driven organisation, we view MuJoCo as โฆSee details»
MuJoCo
We are excited to announce that as of October 2021, DeepMind has acquired MuJoCo and is making it freely available to everyone under the Apache 2.0 license. MuJoCo 2.1 has been โฆSee details»
mujoco/README.md at main ยท google-deepmind/mujoco - GitHub
There are two easy ways to get started with MuJoCo: Run simulate on your machine. This video shows a screen capture of simulate, MuJoCo's native interactive viewer. Follow the steps โฆSee details»
Opening up a physics simulator for robotics - Google DeepMind
Oct 18, 2021ย ยท MuJoCo in DeepMind. Our robotics team has been using MuJoCo as a simulation platform for various projects, mostly via our dm_control Python stack. In the carousel below, โฆSee details»
MuJoCo Overview - Roboti
MuJoCo is a dynamic library with C API, compatible with Windows, Linux and maxOS. It is intended for researchers and developers with computational background. It includes the XML โฆSee details»
GitHub - google-deepmind/mujoco_mpc: Real-time behaviour โฆ
MuJoCo MPC (MJPC) is an interactive application and software framework for real-time predictive control with MuJoCo, developed by Google DeepMind. MJPC allows the user to easily author โฆSee details»
MuJoCo: A physics engine for model-based control - IEEE Xplore
We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive alg.See details»
Open-Source Reinforcement Learning Environments Implemented โฆ
Abstract: This paper presents three open-source reinforcement learning environments developed on the MuJoCo physics engine with the Franka Emika Panda arm in MuJoCo Menagerie. โฆSee details»
Unity Plug-in - MuJoCo Documentation - Read the Docs
The MuJoCo Unity plug-in allows the Unity Editor and runtime to use the MuJoCo physics engine. Users can import MJCF files and edit the models in the Editor. The plug-in relies on Unity for โฆSee details»
Modeling - MuJoCo Documentation - Read the Docs
MJCF models can represent complex dynamical systems with a wide range of features and model elements. Accessing all these features requires a rich modeling format, which can become โฆSee details»
GitHub - openai/mujoco-py: MuJoCo is a physics engine for โฆ
MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3. This library has been updated to be compatible with โฆSee details»
MuJoCo HAPTIX: A virtual reality system for hand manipulation
To fill this gap, we developed a virtual reality system combining real-time motion capture, physics simulation and stereoscopic visualization. The system enables a user wearing a CyberGlove โฆSee details»
Computation - MuJoCo Documentation - Read the Docs
This chapter describes the mathematical and algorithmic foundations of MuJoCo. The overall framework is fairly standard for readers familiar with modeling and simulation in generalized or โฆSee details»
Install and Use MuJoCo on Windows - YouTube
Nov 4, 2023ย ยท This one-minute video will guide you through a streamlined process of installing MuJoCo on Windows, highlighting the simplicity of the task. Thanks to Google DeepMind's โฆSee details»