Individual skill
corl-reproducibility
Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and robot platforms.
CoRL-Skills/skills/corl-reproducibility/SKILL.md
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# CoRL Reproducibility
Robot-learning papers have a split reproducibility problem: the *training* half is
software and can in principle be rerun anywhere, while the *hardware* half is a
physical setup nobody else owns. Strong CoRL papers treat these halves
differently — the software half is made **rerunnable**, the hardware half is made
**auditable** — and say plainly which is which.
## The rerunnable half: simulation and training
The silent reproducibility killers in this field are version-shaped:
- **Simulator versions change physics.** Contact solvers, default damping, and
collision margins shift between releases of MuJoCo, Isaac, PyBullet, and