Robot Data Audit (RDA)

Quality auditing + optimization recommendations for robot datasets. Diagnose data quality issues. Get actionable, confidence-graded suggestions. RDA is a diagnostic tool — it does NOT guarantee training success rate improvements.

RDA audits robot manipulation datasets (LeRobot format) for integrity, temporal consistency, motion quality, and distribution coverage. It then generates optimization recommendations calibrated to your target model architecture.

Features

• 13 quality metrics across 3 tiers: integrity, temporal, motion, and distribution
• rda recommend — Data optimization suggestions calibrated to your model type
  • Frame-wise models (MLP/BC): mild idle trimming suggestions
  • Temporal models (ACT/DP/Transformer): conservative "do not prune" guidance
  • All suggestions include confidence levels (HIGH / EXPERIMENTAL / NOT_RECOMMENDED)
• LeRobot v2.1 + v3.0 dual-format auto-detection
• Three-tier verdicts: PASS / REVIEW / EXCLUDE
• CLI-first design: JSON + text output, pipe-friendly
• Temporal sufficiency analysis: idle detection, active run distribution, valid window ratios

Installation

pip install robot-data-audit

With LeRobot dependency (for .parquet dataset loading):

pip install robot-data-audit[lerobot]

Quick Start

1. Audit a dataset

rda audit /path/to/lerobot/dataset

Runs all 13 metrics, prints a text summary. JSON report saved to <dataset>/rda_report.json.

2. Get optimization recommendations

# For frame-wise models (MLP, BC, etc.)
rda recommend /path/to/dataset --policy frame-wise

# For temporal models (ACT, Diffusion Policy, Transformer)
rda recommend /path/to/dataset --policy temporal

# JSON output for scripting
rda recommend /path/to/dataset --policy frame-wise --format json

What recommend tells you:

• Whether your dataset has excessive idle frames
• Whether trimming is advisable (and how aggressively)
• Model-specific warnings (e.g., "DO NOT prune for temporal models")
• Confidence levels and experimental caveats for every suggestion

3. JSON output & piping

# JSON to stdout
rda audit /path/to/dataset --format json

# Save report to custom path
rda audit /path/to/dataset -o /tmp/my_report.json

# Verbose mode with platform info
rda audit /path/to/dataset --platform so101 -v

4. 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"DHI: {result.quality['dhi']} / 100")

CLI Reference

rda audit

rda audit [OPTIONS] PATH

rda recommend

rda recommend [OPTIONS] PATH

rda example

Show example usage and sample dataset paths.

Exit Codes

Understanding Recommendations

RDA recommendations follow a conservative, evidence-graded approach:

Key principles:

• All suggestions are hypotheses, not guarantees
• Effects vary by task domain and model architecture
• Always validate on a held-out set before applying to training data
• Temporal models (ACT, DP) are generally more sensitive to data trimming

Metrics Overview

Project Structure

rda/
├── cli/          # Click CLI entry points
├── io/           # Data loading and schema definitions (LeRobot v2.1/v3.0)
├── metrics/      # 13 audit metric implementations
├── recommend/    # Optimization recommendation engine
├── audit/        # Dataset and episode-level audit orchestration
└── report/       # Report generation and summary

Development

git clone https://github.com/liesliy/robot-data-audit.git
cd robot-data-audit
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/robot-data-audit}
}

License

MIT