HomeAIOpen-sourcing MuJoCo - Google DeepMind

Open-sourcing MuJoCo – Google DeepMind


In October 2021, we introduced that we acquired the MuJoCo physics simulator, and made it freely accessible for everybody to help analysis all over the place. We additionally dedicated to creating and sustaining MuJoCo as a free, open-source, community-driven challenge with best-in-class capabilities. In the present day, we’re thrilled to report that open sourcing is full and your entire codebase is on GitHub!

Right here, we clarify why MuJoCo is a superb platform for open-source collaboration and share a preview of our roadmap going ahead.

A platform for collaboration

Physics simulators are essential instruments in trendy robotics analysis and sometimes fall into these two classes:

  1. Closed-source, industrial software program.
  2. Open-source software program, typically created in academia.

The primary class is opaque to the person, and though typically free to make use of, can’t be modified and is tough to grasp. The second class typically has a smaller person base and suffers when its builders and maintainers graduate.

MuJoCo is without doubt one of the few full-featured simulators backed by a longtime firm, which is actually open supply. As a research-driven organisation, we view MuJoCo as a platform for collaboration, the place roboticists and engineers can be part of us to develop one of many world’s greatest robotic simulators.

Options that make MuJoCo significantly engaging for collaboration are:

  • Full-featured simulator that may mannequin complicated mechanisms.
  • Readable, performant, moveable code.
  • Simply extensible codebase.
  • Detailed documentation: each user-facing and code feedback.

We hope that colleagues throughout academia and the OSS neighborhood profit from this platform and contribute to the codebase, bettering analysis for everybody.

Efficiency

As a C library with no dynamic reminiscence allocation, MuJoCo may be very quick. Sadly, uncooked physics pace has traditionally been hindered by Python wrappers, which made batched, multi-threaded operations non-performant as a result of presence of the World Interpreter Lock (GIL) and non-compiled code. In our roadmap under, we deal with this difficulty going ahead.

For now, we’d prefer to share some benchmarking outcomes for 2 widespread fashions. The outcomes have been obtained on a regular AMD Ryzen 9 5950X machine, working Home windows 10.

Roadmap

Right here’s our near-term roadmap for MuJoCo:

  • Unlock MuJoCo’s pace potential with batched, multi-threaded simulation.
  • Help bigger scenes with enhancements to inside reminiscence administration.
  • New incremental compiler with higher mannequin composability.
  • Help for higher rendering by way of Unity integration.
  • Native help for physics derivatives, each analytical and finite-differenced.

Be taught extra

Useful assets about MuJoCo:

We sit up for receiving your contributions!



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