We ran RDA across 11 public LeRobot-format datasets — 4,909 episodes in total, spanning sim and real, scripted and human teleop, research arms and $100 hobby hardware. Summary:
| Dataset | Type | Eps | Verdicts (P/R/E) | Action spikes | Idle median | Idle p5 |
|---|---|---|---|---|---|---|
| aloha_sim_insertion_human | sim, human | 50 | 5 / 45 / 0 | 1,338 (100% eps) | 70.7% | 65.1% |
| aloha_sim_transfer_cube_scripted | sim, scripted | 50 | 5 / 45 / 0 | 2,489 (100% eps) | 64.0% | 44.2% |
| aloha_sim_insertion_scripted | sim, scripted | 50 | 1 / 49 / 0 | 1,633 (100% eps) | 63.7% | 53.7% |
| droid_100 | real Franka | 100 | 34 / 66 / 0 | 1,428 (99% eps) | 70.7% | 55.7% |
| pusht | sim | 206 | 43 / 163 / 0 | 1,148 (97% eps) | 81.7% | 59.5% |
| HuggingFaceVLA/libero | sim | 1,693 | 0 / 3 / 1,690 | 34 (3 eps) | 76.5% (3 eps) | 74.6% |
| bridge_orig_lerobot (sampled) | real WidowX | 25 | 4 / 21 / 0 | 91 (84% eps) | 93.3% | 20.5% |
| xarm_lift_medium | real xArm | 800 | 767 / 33 / 0 | 6 (1% eps) | 20.8% | 12.5% |
| xarm_push_medium | real xArm | 800 | 238 / 562 / 0 | 845 (62% eps) | 83.3% | 16.7% |
| svla_so101_pickplace | real SO-100 | 50 | 5 / 45 / 0 | 260 (100% eps) | 86.7% | 59.5% |
| jaco_play | real Jaco | 1,085 | 390 / 695 / 0 | 11,958 (77% eps) | 74.1% | 52.7% |
All integrity layers (NaN/Inf, timestamp validity, missing frames, schema) came back clean on all 11. libero note: 1,690 of 1,693 episodes read 0 frames — a dataset-side meta/layout mismatch, now flagged EXCLUDE instead of silently passing. bridge sampled 25 episodes; the other 10 datasets were audited in full. (aloha_sim_transfer_cube_human was audited earlier at v0.5.1 with matching results: 1/49/0, 1,535 spikes, 71.2% idle — it's now a gated repo, so we couldn't re-pull it.)
Five patterns worth knowing before you train
1. Median idle runs 20.8%–93.3%, and 8 of 11 datasets sit above 65%.
Loss functions trained on a 75%-idle distribution are structurally biased toward predicting "do nothing" unless you weight or curriculum it. Bridge data pushes it to 93%. Measure yours before the GPU bill, not after.
2. Same robot, same lab, four-fold idle difference.
xarm_lift_medium: 20.8% median idle, 767/800 episodes PASS. xarm_push_medium: 83.3% median idle, 562/800 REVIEW. Same xArm platform — the difference is task difficulty (lifting vs. pushing a flat object), not collection sloppiness. High idle isn't always a bug; it's a property you need to know and design around. RDA flags both sides of this honestly.
3. Action discontinuity tracks the controller, not the dataset's reputation.
Sim ALOHA and the SO-100 hobby setup spike in literally 100% of episodes; xArm lift data has 6 spikes across 800 episodes. If your policy uses smoothness regularization or you're doing sim-to-real action statistics, this number decides your curriculum.
4. Clean integrity ≠ good training data.
Integrity passed 11/11 — zero NaNs, zero timestamp reversals, zero missing frames anywhere. The behavior layer still flagged 45–98% of episodes for review in most datasets. Both layers matter; most pipelines check neither.
5. Cheap hardware produces the most expensive data.
The community SO-100 pick-place set: 86.7% median idle plus spikes in every episode. If you're fine-tuning on hobby-robot uploads, this is what you're inheriting.
Reproduce
pip install robot-data-audit
rda audit <any lerobot dataset> -v
rda recommend <dataset> --policy temporal # or frame-wise, --lang en
Tool: https://github.com/liesliy/rda · PyPI: robot-data-audit · UI: rda ui (EN/中文)
Caveats: RDA flags statistical anomalies, not ground-truth errors. REVIEW means "look before you train," not "discard." All thresholds are open for debate — that's what the issue tracker is for.
