Robot Data Audit (RDA)

Audit robot dataset quality. Local only.

RDA checks LeRobot-format datasets for data integrity, temporal consistency, and motion anomalies. It flags problematic episodes and reports what's wrong. RDA is a diagnostic tool only — it does not guarantee training success rate improvements.

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Quick Start

pip install robot-data-audit           # install
rda audit /path/to/dataset             # audit → text + JSON report in <dataset>/rda_report.json

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Sample Output

$ rda audit ~/datasets/my_robot_data

  ── Verdict ──
  PASS:    180 (90.0%)
  REVIEW:   15 ( 7.5%)
  EXCLUDE:   5 ( 2.5%)

  ── Top Issues ──
  1. [HIGH ★] Action discontinuity: 3421 spikes across 195 episodes
  2. [MEDIUM] High idle ratio: median 72% idle, 28% effective motion
  3. [LOW] Extreme acceleration spikes: 2847 across 180 episodes

Output options: --format json for scripting, -o FILE to save elsewhere.

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Real Audit Results

Audited on ArmnetBench (SO-101 arm, 2,499 episodes) and DROID (100 episodes):

Metric	ArmnetBench (200 ep subset)	DROID (100 ep)
Action spikes detected	5,229	1,428
Median idle ratio	68.6%	70.7%
Median episode duration	22.4s	15.0s
Verdict	All PASS	All PASS

Both are curated benchmark datasets — all episodes pass. RDA also runs on noisier, real-world collections where REVIEW/EXCLUDE verdicts appear more frequently.

Full calibration analysis (ArmnetBench, comparing successful vs failure episodes by label): docs/ARMNETBENCH_CALIBRATION_REPORT.md

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Design

• Local only — No data leaves your machine. Runs entirely offline.
• Diagnostic, not predictive — RDA identifies data issues. Whether fixing them improves training is a separate question and depends on your task, model, and setup.
• Statistical anomaly detection — Uses MAD on reference distributions instead of fixed thresholds (no hardcoded 3σ rules). Adapts to each dataset's characteristics.
• Universal core metrics — Primary ranking uses 3 platform-independent metrics (duration, spike_count, effective_motion_ratio). Platform-specific signals (velocity, path_length) are optional diagnostics.

See docs/MVP_PRODUCT_SPEC.md for full metric definitions.

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Metrics

Tier	Metric	Detects	Cross-platform?
L1	Timestamp monotonicity	Clock resets, duplicates	✅
L1	Frame interval consistency	Irregular sampling	✅
L1	Schema compliance	Missing/extra fields	✅
L2	Temporal gap detection	Time discontinuities	✅
L2	Sensor synchronization	Multi-sensor drift	⚠️
L2	Temporal sufficiency	Idle vs active structure	✅
L3	Velocity spikes	Implausible jumps	️
L3	Motion discontinuities	Jerky trajectories	⚠️
L3	Idle frame detection	Paused segments	✅
L4	Duration outliers	Too short / too long	✅
L4	Spike count outliers	Unusual jerk profiles	✅
L4	Effective motion ratio	Low-activity episodes	✅

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CLI Reference

rda audit

rda audit /path/to/dataset [OPTIONS]

Option	Description
-o, --output FILE	Save JSON report (default: <path>/rda_report.json)
--format [json|text]	Output format (default: text)
--platform TEXT	Robot platform name for Tier 3 normalization
-v, --verbose	Verbose output

Exit codes: 0 = no EXCLUDE, 1 = error, 2 = at least one EXCLUDE.

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Python API

from rda.audit.dataset_audit import DatasetAuditor
from rda.io.lerobot_loader import iter_episodes, load_lerobot_dataset

dataset_info = load_lerobot_dataset("/path/to/dataset")
auditor = DatasetAuditor()
result = auditor.audit_dataset(dataset_info, iter_episodes("/path/to/dataset"))
print(f"PASS: {result.verdict_counts['PASS']}")

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Development

git clone https://github.com/liesliy/rda.git
cd rda
pip install -e ".[dev]"
pytest

Citation

@software{robot_data_audit,
  title     = {Robot Data Audit: Quality Auditing for Robot Manipulation Datasets},
  author    = {Niu Su Tech},
  year      = {2026},
  url       = {https://github.com/liesliy/rda}
}

License

MIT