sim2real-calibration

name	sim2real-calibration
description	Sim-to-real calibration skill for robot-dog virtual prototypes. Use this skill when comparing simulation logs against physical prototype logs, estimating friction/mass/latency/torque scale/contact parameter corrections, reducing sim-real gap, or generating parameter updates for MuJoCo/PyBullet/digital twin.

sim2real-calibration

This skill compares simulation metrics with measured prototype metrics and proposes conservative parameter updates. The MVP is file-based and does not connect to hardware. It helps reduce the sim-real gap after a physical prototype exists.

When To Use

Use this skill for:

• Comparing simulated and measured speed, slip, torque, roll/pitch, and latency.
• Producing parameter update suggestions for simulation/MuJoCo.
• Blocking digital-twin trust when sim-real error is too high.
• Creating calibration reports after physical testing.

Workflow

1. Read <project>/calibration_dataset.yaml.
2. Compare simulation and real metrics against tolerances.
3. Estimate conservative parameter updates.
4. Write calibration report artifacts into <project>/reports/.

Commands

python skills/sim2real-calibration/scripts/compare_logs.py skills/sim2real-calibration/examples/quadruped_mvp
python skills/sim2real-calibration/scripts/propose_parameter_update.py skills/sim2real-calibration/examples/quadruped_mvp

Rules

• Never claim calibrated parameters are validated without a follow-up simulation and physical re-test.
• Treat missing real logs or excessive error in required metrics as blockers.
• Keep updates conservative and bounded.
• Do not import sibling subskill code. Read files only.

References