Robot Data Audit (RDA)

Independent quality assessment for robot data. Runs locally — your data never leaves your machine.

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, then generates optimization recommendations calibrated to your target model architecture. Use it as an independent check before you accept a vendor dataset, train a policy, or publish a benchmark.

RDA audit overview

Install

pip install robot-data-audit

Optional dependency tiers:

Tier	Extra	Unlocks
Core (default)	—	parquet audits: integrity + temporal/motion + dataset-utility metrics
Visual	`pip install robot-data-audit[video]` or `pip install av`	the 4 visual metrics (freeze / timestamp-alignment / stream-sync / quality)
Lerobot	`[lerobot]`	`.parquet` dataset loading via the lerobot package
UI	`[ui]`	the web dashboard

Visual metrics without PyAV are reported as "not audited", never as "pass": the JSON report carries a top-level skipped_by_missing_dep field and the CLI prints a warning listing the skipped checks. The web dashboard shows the same guarantee — a dep-missing dataset renders a "not audited ≠ pass" banner, and the Health Overview adds a Visual Integrity section (checked / flagged / not-checked episode counts) while Episode Explorer gains a per-episode Visual Audit panel (freeze-region table, per-camera quality penalties, per-sample quality curves).

Quick start

# 1. Audit a dataset — 18 metrics incl. visual-stream integrity, three-tier verdicts (PASS / REVIEW / EXCLUDE)
rda audit /path/to/lerobot/dataset

# 2. Recommendations calibrated to your model type
rda recommend /path/to/dataset --policy temporal   # or frame-wise

# 3. Optional web dashboard
rda ui

rda audit is fully offline. rda recommend computes all metrics locally and sends only aggregated statistics (<1KB) to the rules API — cached for offline reuse, and RDA_API_URL can point to your own server for private deployments. The usable_retention statistic is fps-aware (v0.7.3): a "usable" run must span ≥16 frames and ≥1 second (DROID's min_non_idle_len=16 encodes 1 s at 15-30 Hz; on 50 Hz data the floor rises to 50 frames so sub-second twitches no longer count).

RDA recommendations

Why RDA

12 local datasets, 4,959 episodes, one set of default thresholds, zero per-dataset tuning — full table in docs/benchmark.md.

Full audit of lerobot/libero_10 (v3.0) — 379 episodes, 101,469 frames: all 12 applicable integrity checks clean, 0 hard defects; the one REVIEW signal (low-motion heuristic) is discussed honestly. Read the report →

Blind test — we injected 50 defective episodes (5 defect classes, seed=42) into lerobot/pusht and kept 156 as controls. RDA caught all 50 under the broad criterion, precision 1.000 (zero false alarms on controls) under the strict one. Read the blind-test report →

Validated on AgiBotWorld2026 — third-party audit of AgiBot's Phase 3 dataset: all 5 simulation tasks + a real-robot RL package, 1,112 episodes, 4,448 integrity checks with 0 failures, and a 3.1× enrichment of RDA's discontinuity spikes at official human-takeover boundaries. Zero adaptation needed. Read the case study →

Every audit can also render into a shareable single-file HTML report and a README badge:

python tools/rda_render.py rda_report.json --html report.html --badge badge.svg

More: CLI reference & metrics table · experiments · real-world feedback form

Metric provenance

Every metric ships with a four-file provenance record (docs/provenance/<metric>/): algorithm.md (how it works), source.md (public precedents consulted — ideas only), implementation_origin.md (original implementation, zero third-party code), license.md (compliance notes). Covered: all 18 metrics (missing_dropout, invalid_values, schema_consistency, timestamp_validity, joint_limit, video_frame_integrity, video_freeze, video_timestamp_alignment, video_stream_sync, visual_quality, sensor_synchronization, sampling_jitter, velocity_acceleration, action_discontinuity, temporal_sufficiency, idle_ratio, distribution, coverage). Index: docs/provenance/.

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}
}

MIT License.

Release files for robot-data-audit 0.7.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for robot-data-audit 0.7.3
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robot_data_audit-0.7.3.tar.gz	205.9 kB	Sep 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for robot-data-audit 0.7.3
File	Interpreter	ABI	Platform	Reset
robot_data_audit-0.7.3-py3-none-any.whl 204.0 kB Sep 5, 2026	Python 3	none	any	Details

Total release size: 409.9 kB

Release files / robot_data_audit-0.7.3.tar.gz

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Release files / robot_data_audit-0.7.3-py3-none-any.whl

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Size	204.0 kB
Tags	Python 3
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Uploaded via	`twine/7.0.0 CPython/3.13.14`