I’m a beginner in robot simulation, and I often use AI to help me create MuJoCo environments.
That is useful, but it also exposes a common problem: an AI assistant can generate a scene that looks plausible in XML, but fails when you actually load or simulate it. A robot may start in an impossible pose. A box may float. A gripper may be missing. A humanoid may immediately fall over. The scene technically exists, but it is not a useful simulation environment.
That is why I created mujoco-skills:
https://github.com/coolbeevip/mujoco-skills
It is a MuJoCo skill for AI agents. The idea is to give an AI assistant a more disciplined workflow for creating MJCF scenes, checking them, opening the viewer, inspecting actuators, and running small control experiments.
The repository includes examples such as:
- Franka Panda pick scene
- UR5e sorting scene
- Unitree Go1 obstacle scene
- Hello Robot Stretch tabletop scene
- Unitree H1 humanoid walking layout
The README examples and scene checks were validated with Codex GPT-5.5.
This is not a magic “generate perfect robot simulation” tool. It is closer to a practical helper layer for people who want to use AI while learning MuJoCo. It tries to make the assistant ask better questions, run checks, and catch obvious physical mistakes before handing off a scene.
If you are also experimenting with AI-assisted robotics simulation, I would appreciate feedback:
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