mujoco-simulation

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MuJoCo-oriented robot-dog dynamics validation, MJCF scenario contracts, contact, terrain, actuator, stability, torque, slip, and disturbance checks for virtual prototypes. Use this skill when the user asks for MuJoCo, MJCF, high-fidelity legged-robot simulation, contact/friction validation, slope/step/drop/push scenarios, or serious gait simulation beyond PyBullet smoke tests.

baibai2013 By baibai2013 schedule Updated 6/8/2026

name: mujoco-simulation description: | MuJoCo-oriented robot-dog dynamics validation, MJCF scenario contracts, contact, terrain, actuator, stability, torque, slip, and disturbance checks for virtual prototypes. Use this skill when the user asks for MuJoCo, MJCF, high-fidelity legged-robot simulation, contact/friction validation, slope/step/drop/push scenarios, or serious gait simulation beyond PyBullet smoke tests.

mujoco-simulation

This skill defines and evaluates MuJoCo-style dynamics scenarios for robot-dog virtual prototypes. The MVP uses deterministic scenario metadata so tests and digital-twin gates can run without a local MuJoCo install. It establishes the file contract that later real mujoco runners will fill with solver output.

When To Use

Use this skill for:

  • MJCF/MuJoCo scenario planning and validation.
  • Stand, flat-walk, slope, step-obstacle, drop, and push-disturbance checks.
  • Contact/friction, foot slip, body roll/pitch, torque margin, and fall checks.
  • Generating mujoco_result.json for robot-dog-digital-twin gates.

Workflow

  1. Read <project>/mujoco_scenarios.yaml.
  2. Evaluate each scenario against stability, posture, contact, torque, slip, and energy limits.
  3. Write per-scenario *.sim_result.json plus a project-level mujoco_result.json.
  4. Mark blockers when any required scenario falls, exceeds posture/contact limits, or violates torque/slip thresholds.

Commands

python skills/mujoco-simulation/scripts/run_scenarios.py skills/mujoco-simulation/examples/quadruped_mvp
python skills/mujoco-simulation/scripts/summarize_results.py skills/mujoco-simulation/examples/quadruped_mvp

Rules

  • Keep the MVP deterministic and conservative.
  • Do not claim metadata-mode results are real MuJoCo solver output.
  • Use PyBullet simulation for lightweight CI smoke; use this skill for MuJoCo/MJCF contracts and higher-fidelity scenario gates.
  • Do not import sibling subskill code. Read files only.

References

  • references/input-contract.md for mujoco_scenarios.yaml fields.
  • references/scenarios.md for MVP scenario definitions.
  • references/output-contract.md for report fields.
  • references/backend-plan.md for moving from metadata backend to real MuJoCo.
  • references/examples.md for sample project layout.
Install via CLI
npx skills add https://github.com/baibai2013/build123d-cad --skill mujoco-simulation
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