iros-experiments
Use when designing or auditing IROS experiments — real-robot trial counts, success criteria set in advance, reset procedures, failure taxonomies, baseline fairness on matched hardware, sim-to-real gap reporting, small-n statistics, and the claim-to-evidence ladder that embodied-systems reviewers apply before they trust a demo.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iros-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# IROS Experiments Use this before submission when the empirical story is not yet locked. At IROS, experiments exist to prove a robot did something reliably, under stated conditions — not to top a leaderboard. ## Experiment audit - Map each claim to the evidence that supports it: a real-robot trial set, a simulation study, an ablation, or a field deployment. A claim with no matched evidence is an overclaim. - Define the **success criterion before running**, in one sentence a skeptic would accept, and hold to it; a criterion invented after seeing results is a reviewer red flag. - Report **trial counts and resets**: how many attempts, how start conditions were randomized, and what reset happened between trials. Hidden resets inflate reliability. - Publish a **failure taxonomy** with counts, not just a success rate; the failures are where reviewers calibrate trust. - Make **baselines fair**: run them on the same platform, sensors, and route, with comparable tuning effort, or state precisely why not. - Separate **simulation from real** and report the **sim-to-real gap** as a number; an implied zero gap is the fastest way to lose a reviewer. ## The evidence ladder | Claim altitude | Evi
- Experiment audit
- The evidence ladder
- Small-n statistics for real robots
- Vignette: a manipulation reliability study
- Reporting floor
- Output format
What does the iros-experiments skill do?
Use when designing or auditing IROS experiments — real-robot trial counts, success criteria set in advance, reset procedures, failure taxonomies, baseline fairness on matched hardware, sim-to-real gap reporting, small-n statistics, and the claim-to-evidence ladder that embodied-systems reviewers apply before they trust a demo.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iros-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.