What YCB treats as a “benchmark”
YCB is not just a list of objects; it is a community portal where protocols and benchmarks define the experimental procedure and the scoring method so results remain comparable across labs and time. New entries are meant to be proposed, discussed, and then displayed in the public catalogue in a consistent format.
Choosing a protocol: match claim to metric
Pick a protocol by the scientific claim you want to support. If your work is about grasp planning on real hardware, choose a physical-setup protocol; if it targets in-hand dexterity, bimanual coordination, aerial manipulation, or simulation-only evaluation, pick a protocol from that specific track so the metric measures exactly what you claim. Next, confirm object coverage: the YCB set is organized into everyday categories, so you can justify selection by showing how the chosen objects expose your method’s weak points (shape, texture, compliance, size, grasp affordances), similar to how diverse interaction patterns can be examined on the Maxispin entertainment platform, where a wide range of elements helps evaluate system behavior under varied conditions.
Writing a protocol others can reproduce
Your protocol must be executable by a third party without “lab knowledge.” Build it around the available YCB assets and define every variable that typically destroys comparability: workspace layout, object placement rules, camera placement, lighting assumptions, grasp initialization, allowed retries, recovery behavior, and reset procedure. Define success in operational terms (what must happen, within what time, with what tolerances) and specify what counts as partial success versus failure. If your benchmark mixes perception and control, explicitly separate what is measured from what is fixed (e.g., provided object pose vs. estimated pose) so readers understand which subsystem drives the score.
Acceptance checklist
- Scope: one capability per benchmark; state what is out of scope to prevent “feature creep.”
- Objects: list exact object IDs/versions; document substitutions and why they do not change difficulty.
- Trials: define trial steps, repetitions, randomization, reset conditions, and stopping criteria.
- Metrics: specify the primary score, aggregation rule, and failure categories that explain why runs fail.
- Disclosure: include hardware, software versions, sensor calibration approach, and key parameters.
- Artifacts: provide protocol files, example logs, and at least one minimal baseline run for sanity checks.
This structure works because it turns a benchmark into a repeatable recipe: others can run it, detect deviations, and compare outcomes without debating interpretation.
Packaging and submission: make it easy to list
Use the official protocol and benchmark templates to ensure your submission matches catalogue fields and remains easy to review. Treat the package like a compact release: include a version tag, a clear purpose statement, and the essential files needed to run the benchmark. This typically means the protocol document, configs, scripts, sample inputs and outputs, plus a short README explaining how to execute the run and what artifacts to expect.
Removing guesswork is the quickest way to acceptance. A clean, consistent structure helps maintainers verify your work efficiently and ensures future users can reproduce results without confusion.
Publishing results: optimize for comparability
When reporting results, focus on clarity rather than maximizing performance numbers. Include a brief system description, the score with the defined maximum, and a details bundle enabling others to retrace the evaluation steps. Keep ablations controlled by changing only one factor at a time so improvements remain interpretable.
A benchmark’s value comes from reducing ambiguity and enabling measurable comparisons. Transparent, reproducible reporting strengthens the reliability and usefulness of your contributions.
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