How Two GitHub Issues Shaped RDA v0.9.14: Action Units, Per-Joint Analysis, and Smarter Freeze Detection

Описание и применение

Две GitHub‑проблемы привели к выпуску RDA v0.9.14, который теперь корректно определяет единицы измерения действий, выводит дискретность по суставам, гарантирует масштабную инвариантность и добавляет кросс‑валидацию заморозки видео с использованием данных состояния.

Задача

Robot learning datasets often contain action data in inconsistent units, making spike detection unreliable and hindering dataset quality assessment.

Результат

RDA v0.9.14 improves data quality diagnostics by correctly handling action units, exposing granular per‑joint spike data, ensuring scale invariance, and enabling state‑based freeze detection, all driven by real user feedback.

Рабочий процесс

• 1. User files GitHub issues with reproducible analyses.
• 2. RDA developers review issues, run tests, and confirm findings.
• 3. RDA code is updated to infer action units, expose per‑joint data, add scale‑invariance guard test, and implement freeze‑motion‑source flag.
• 4. New version is released on PyPI and GitHub.
• 5. Users run RDA v0.9.14 on their datasets and interpret updated reports.

Доказательства

We ran the test properly. We took the same svla action data and applied four different scale transforms… The result: spike_count was 260 in all four cases.

Источник

In v0.9.14, we moved by_joint to the top-level measurement output… Running v0.9.14 on the full svla dataset gives per-joint totals… very close to the developer's independent calculation.

Источник