I built a MuJoCo skill for AI agents after using AI to create simulation scenes as a beginner
Hi everyone,
I’m the author of this repository:
https://github.com/coolbeevip/mujoco-skills
I’m still a beginner in robot simulation, and one thing I do a lot is ask AI to help me create MuJoCo environments. That works surprisingly well sometimes, but it can also fail in very physical ways: objects floating, robots starting in impossible poses, grippers missing, humanoids collapsing, or scenes that load but do not make sense.
So I started building a MuJoCo skill for AI agents. The goal is not to replace MuJoCo knowledge, but to give an AI assistant a better workflow when creating and checking MJCF scenes.
It currently helps with:
- MJCF scene construction
- basic physical sanity checks
- viewer startup
- actuator inspection
- small control experiments
- example scenes such as Franka pick, UR5e sorting, Go1 obstacle crossing, Stretch tabletop manipulation, and H1 humanoid walking layout
The README examples and scene checks were validated with Codex GPT-5.5.
I’m sharing it because I think many beginners have a similar need: using AI to get a first working simulation scene, then gradually learning enough MuJoCo to improve it.
Feedback is welcome, especially from people who have stronger MuJoCo or robotics simulation experience.