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corl-experiments
Use when designing or auditing experiments for a CoRL robot-learning paper — seeds and evaluation-episode counts, task-suite breadth, real-robot versus simulation evidence, sim-to-real gap measurement, baseline fairness across BC/RL/VLA families, generalization splits, and statistics for success-rate claims.
CoRL-Skills/skills/corl-experiments/SKILL.md
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# CoRL Experiments
At CoRL the object under evaluation is a *learned policy*, which makes the
evidence problem statistical twice over: training is stochastic (seeds, data
order, initialization) and execution is stochastic (initial states, physics,
sensor noise). An experimental design that controls only one of the two is the
most common weakness this reviewer pool writes up.
## Match the evidence to the claim, not the venue
| Claim in the paper | Minimum credible evidence shape |
|---|---|
| "Method X learns task family T" | Multiple training seeds; per-task success over many scripted-reset episodes |
| "X outperforms baseline Y" | Same data, same evaluation protocol, same tuning effort for both; dispersion reported |