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

说明与用途

两条 GitHub 问题促成了 RDA v0.9.14 的发布，现已正确推断动作单位、公开每关节的尖峰计数、保证尺度不变性，并新增基于状态的冻结交叉验证。

问题

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

结果

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.

工作流

• 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.

证据

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.

来源

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.

来源