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

Small-n statistics for real robots

Real trials are expensive, so n is small — but small n does not excuse a bare mean. Report a success count as a proportion with a confidence interval (a Wilson interval behaves better than normal approximation at small n), and for paired system-vs-baseline comparisons on the same trials, prefer a paired test over independent means. State n every time; "usually succeeds" is not a measurement.

Vignette: a manipulation reliability study

Suppose the system claims reliable grasping of unseen objects. The matching plan: fix an object set and a success criterion (lifted and held 3 seconds), run a stated number of trials per object with randomized poses, log every failure by cause (slip, mis-localization, collision), and report the per-object and pooled success rates with intervals. A simulation sweep over object mass then maps where the grasp model degrades, and the real-vs-sim gap is stated — every panel tied to a specific claim.

Trial-logging template (one row per attempt):
  trial_id, object/scenario, start_pose_seed, outcome{success|fail},
  failure_cause, reset_type, wall_time, notes
Aggregate to: success rate + interval per condition, failure histogram, sim-to-real delta.

Reporting floor

• Every reliability figure carries n and a criterion; every timing claim carries a measured rate and the compute/power budget it ran under.
• Report the compute actually consumed on the robot, not a desktop proxy.

Output format

[Experiment readiness] strong / adequate / weak
[Claim -> evidence map] <claim: trials/sim/ablation/deployment>
[Missing evidence] <trials/resets/failures/baseline/transfer>
[Statistics] criterion set? interval reported? paired where paired?
[Decision-critical next run] <one experiment on the robot>