mujoco
JuliaInterpreter.jl
mujoco | JuliaInterpreter.jl | |
---|---|---|
20 | 5 | |
7,213 | 157 | |
2.1% | 0.6% | |
9.9 | 7.6 | |
about 22 hours ago | 27 days ago | |
Jupyter Notebook | Julia | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mujoco
- MuJoCo 3
-
Mujoco Question
I have installed mujoco-2.3.5-window-x86_64.zip and Source Code (zip) in https://github.com/deepmind/mujoco/releases. From download file, I extracted file and clicked simulate (mujoco-2.3.5-window-x86_6 -> bin); however it shows black screen. I am not sure what I am doing wrong.
-
Python Tutorials
Best place for technical MuJoCo questions is our GitHub repo (https://github.com/deepmind/mujoco) -- our entire team is notified for each issue posted there.
-
Mujoco + Unity => setting transform
Could you please post this question on https://github.com/deepmind/mujoco also? You'd me more likely to get an answer there since most of our team don't track Reddit questions.
-
Hi, i guys i have been working on bouncing ball experiment in Mujoco and i have had a fairly realistic effect of a ball bouncing, however i want it to be bouncing forward like tossing ball and it bounces forward? how can i achieve this? my XML is below
Could you please post this as an issue on https://github.com/deepmind/mujoco ?
-
MuJoCo Soft Surface Problem
Try posting in https://github.com/deepmind/mujoco/discussions ?
-
Deep RL with Mujoco environments using Docker on Apple Silicon
Not really. You can use conda or pip to install prebuilt mujoco packages that also include glfw. Check out https://github.com/deepmind/mujoco/blob/main/python/README.md
-
DeepMind's open-source version of MuJoCo available on GitHub
MuJoCo Github link
- MuJoCo Physics
- DeepMind open-sources MuJoCo Physics
JuliaInterpreter.jl
-
Do you use Julia for general purpose tasks?
The projects page is a list of suggestions of projects that someone has already said they want to run. If you can find a mentor, you can submit a project for anything. For potential performance improvements, I'd look at https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/206, https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/312, and https://github.com/JuliaDebug/JuliaInterpreter.jl/issues/314. I'm not sure if Tim Holy or Kristoffer have time to mentor a project, but if you're interested in doing a gsoc, ask around in the Julia slack/zulip, and you might be able to find a mentor.
-
Julia 1.7 has been released
I would not go as far as calling it very naive, there has certainly been some work put into optimizing performance within the current design.
There are probably some gains to be had by using a different storage format for the IR though as proposed in [1], but it is difficult to say how much of a difference that will make in practice.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl/pull/309
-
What's Bad about Julia?
You're right, done some more research and there seems to be an interpreter in the compiler: https://github.com/JuliaDebug/JuliaInterpreter.jl. It's only enabled by putting an annotation, and is mainly used for the debugger, but it's still there.
Still, it still seems to try executing the internal SSA IR in its raw form (which is more geared towards compiling rather than dynamic execution in a VM). I was talking more towards a conventional bytecode interpreter (which you can optimize the hell out of it like LuaJIT did). A bytecode format that is carefully designed for fast execution (in either a stack-based or register-based VM) would be much better for interpreters, but I'm not sure if Julia's language semantics / object model can allow it. Maybe some intelligent people out there can make the whole thing work, is what I was trying to say.
-
Julia: faster than Fortran, cleaner than Numpy
It could, but that is a lot more work than it sounds. It might be easier to make it possible to swap out the compiler for one that is much faster (LLVM is slow but does good optimisations, other compilers like cranelift are faster but produce slower code). There is a Julia interpreter but it was written in Julia itself (it was written to support debuggers), so it doesn't really solve the latency issues.
-
Julia: Faster than Fortran, cleaner than Numpy
If you need to run small scripts and can't switch to a persistent-REPL-based workflow, you might consider starting Julia with the `--compile=min` option. You can also reduce startup times dramatically by building a sysimg with PackageCompiler.jl
There is also technically an interpreter if you want to go that way [1], so in principle it might be possible to do the same trick javascript does, but someone would have to implement that.
[1] https://github.com/JuliaDebug/JuliaInterpreter.jl
What are some alternatives?
gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
Diffractor.jl - Next-generation AD
Bullet - Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
brax - Massively parallel rigidbody physics simulation on accelerator hardware.
Tullio.jl - ⅀
CHRONO - High-performance C++ library for multiphysics and multibody dynamics simulations
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
sofa - Real-time multi-physics simulation with an emphasis on medical simulation.
Infiltrator.jl - No-overhead breakpoints in Julia
LiquidFun - 2D physics engine for games
rust - Empowering everyone to build reliable and efficient software.