When We Audited Someone Else's Robot Dataset on GitHub, the Hardware Maker Responded

说明与用途

自动化机器人数据集审核在缺乏采集上下文时可能产生误导性指标。与硬件制造商的交流表明，高峰值和高空闲比率是采集方法的特征，而非数据缺陷。RDA 现在包含控制模式元数据和透明阈值。

问题

Automated quality audits of robot datasets can produce misleading metrics if collection context is ignored, leading to incorrect conclusions about data quality.

结果

The audit revealed that high spike counts were due to non-interpolated data collection, not data corruption. High idle ratios are normal for manipulation tasks. RDA now includes control mode metadata and transparent thresholds, improving metric interpretability.

工作流

• 1. Run RDA v0.9.12 on target datasets.
• 2. Review audit report for spike counts, idle ratios, and other diagnostics.
• 3. Open a GitHub Issue with the report on the dataset repository.
• 4. Await feedback from hardware manufacturer or dataset authors.
• 5. Interpret metrics in context of collection mode and task type.
• 6. Update RDA to include control mode metadata and gap_multiplier_used.
• 7. Re-run audit and compare results.
• 8. Adjust data collection strategy (e.g., use mode 6/7) if needed.

证据

If operated with non-interpolated mode, then the spikes would appear easily.

来源

We normally recommend collecting data with online trajectory planning modes (6 or 7) to utilize the dynamic interpolation and velocity control in our control box.

来源