| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| tags: |
| - LeRobot |
| - so101 |
| - smolvla |
| - teleoperation |
| - egocentric |
| - retargeting |
| - ISR |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # so101-smolvla-data — two SO-101 halves in LeRobot v3.0, ready for SmolVLA |
|
|
| ``` |
| std_mm_teleop_v30.tar 95 MB 100 eps 9,500 frames 1 task ISR-standardized real teleop |
| ego_v30.tar 319 MB 324 eps 36,442 frames 89 tasks retargeted egocentric video |
| std_results/ the standardization run that produced the first half |
| ``` |
|
|
| Both tars unpack to a complete **LeRobot v3.0** tree (`meta/ data/ videos/`) that loads with |
| `LeRobotDataset(repo_id, root=...)`. Same 6-DOF SO-101 layout, same camera keys, same units. |
|
|
| ## The two halves |
|
|
| | Field | `std_mm_teleop_v30` | `ego_v30` | |
| |---|---|---| |
| | Format | LeRobot **v3.0** | LeRobot **v3.0** ✓ | |
| | Robot | `so_follower` | `so101_follower` (same arm, different string) | |
| | Rate | **10 fps** (30 Hz ÷ 3.05 ISR compression) | **30 fps** | |
| | Episodes | **100** | **324** | |
| | Frames | **9,500** (from 28,948) | **36,442** | |
| | Tasks | **1** — "pick up blue cube and put into orange box" | **89** | |
| | Duration | 15.8 min nominal (real span 16.1 min) | 20.2 min | |
| | Episode length | mean 95, range 78–117 | mean 112.5, range 30–354 | |
| | Camera keys | `observation.images.front` / `.wrist` | same ✓ | |
| | Video | 640×480, h264, yuv420p | 640×480, h264, yuv420p ✓ | |
| | Image source | **real** cameras (openbooth SO-101 rig), ISR-selected subset, re-encoded | **synthesized** — 2 crops from 1 ego camera | |
| | state / action | `float32[6]` | `float32[6]` ✓ | |
| | State layout | `[pan, lift, elbow, wrist_flex, wrist_roll, gripper]`, **degrees** | same ✓ | |
| | Action convention | teleop **command**, leads state by ~4 raw frames (0.13 s, residual 0.90°); 2 steps / 2.08° after ISR | absolute **next-frame target** — exact, max abs diff **0.0** on all 324 eps | |
| | Gripper encoding | deg, **larger = open** (checked against the wrist video: 1.40° = jaws touching, 59.78° = wide), range 1.4–59.8 | deg, **larger = open**, range −10.0–84.3 — same polarity ✓ | |
| | Gripper occupancy | rests **closed** — mean 0.294 of span, 52.6 % of frames in the bottom quarter, 4.1 % in the top | rests **mid** — mean 0.328, 33.5 % bottom quarter, 0.7 % top | |
|
|
| Every row is measured off the files (`std_results/stats_compare.py`, ffprobe on the mp4s, the |
| parquet columns); raw numbers in `std_results/comparison_stats.json`. |
|
|
| ### What to watch when co-training |
|
|
| 1. **10 vs 30 fps.** ISR frames are non-uniform in real time, so no single fps is literally true; |
| 10 is the measured effective rate and keeps episode durations within ~2 % of the real ones. |
| A shared action horizon in *seconds* therefore covers 3× more steps on the ego half. |
| 2. **Action convention.** On ego, `action[t] == state[t+1]` exactly — no controller dynamics to |
| learn. The teleop half carries a genuine ~0.13 s tracking lag, and ISR does not remove it. |
| 3. **Gripper span** differs (1.4–59.8° vs −10.0–84.3°) at identical polarity, so per-dataset |
| normalization differs; binarizing at each dataset's own closed→open midpoint is the safe fix. |
| 4. **1 vs 89 tasks**, real vs synthesized imagery. |
|
|
| ## Provenance |
|
|
| | half | source | processing | |
| |---|---|---| |
| | `std_mm_teleop_v30` | `makermods/2nd_100ep_blue_cube_orange_box` (LeRobot v3.0, real SO-101 teleop) | ISR standardization (`teleop_std_poc`) → kept frames rewritten as a v3.0 dataset | |
| | `ego_v30` | `angkul07/ego-data` (EgoDex), retargeted through DT-pipeline stage 6 run F | LeRobot v2.1 → v3.0 (metadata + ffmpeg concat, **stream copy** — pixels untouched) | |
|
|
| ## `std_results/` — the standardization run |
| |
| ISR (*Information-Standardized Trajectory Resampling*, Yang et al., IROS 2026, |
| [arXiv:2606.22907](https://arxiv.org/abs/2606.22907)) keeps one frame per fixed amount of |
| **information** (distance moved + accumulated acceleration) instead of one frame per fixed amount |
| of time, so operator pauses collapse and contact-rich moments stay dense. Demonstration consistency |
| is then scored with ActionVariance (Eq. 9 of |
| [arXiv:2306.02437](https://arxiv.org/abs/2306.02437)) and episodes are bucketed. |
| |
| ``` |
| out/report.json final artifact: per-episode bucket + ISR stats + thresholds |
| out/scores_raw.json ActionVariance before ISR |
| out/scores_isr.json ActionVariance after ISR |
| out/eps_sensitivity.json cluster-radius stability check |
| out/isr/ per-episode kept indices + resampled arrays + 3x uniform baseline |
| out/plots/ compression / spacing / variance + 12 per-episode figures |
| RUN_NOTES.md knobs, calibration, results, caveats |
| calibrate.py, eps_sens.py, build_std_dataset.py, stats_compare.py |
| ``` |
| |
| **Results.** 28,948 → 9,500 frames (32.8 %). The kept ratio varies **25.1–44.1 %** per episode |
| against a flat 33.4 % for the 3× time-uniform baseline — that content-adaptivity is ISR's claim, |
| and it reproduces here. Buckets (P50/P90 on ISR scores): **50 train / 40 review / 10 quarantine**. |
| |
| Two caveats that belong next to those numbers: |
| |
| - **ActionVariance rose raw→ISR** (89.8 → 94.3, up in 76/100 episodes), the opposite of the |
| BridgeData POC. Expected rather than broken: dropping pause frames removes the *lowest*-variance |
| samples. Read the ISR column alone, as a within-dataset ranking. |
| - **Buckets are ε-sensitive.** All 100 episodes are one task, so states overlap heavily; per-episode |
| rank correlation against ε=0.5 falls to 0.51 (ε=0.3), 0.41 (0.2), 0.19 (0.1). Treat the 10 |
| quarantined episodes as a shortlist to eyeball, not a verdict. |
| |
| Knobs were recalibrated for degree units (the defaults assume metres): `d_target=12`, |
| `λ_acc=0.002`, `gripper-threshold=2.0°`. See `RUN_NOTES.md`. |
|
|
| The ActionVariance scorer is a from-the-paper implementation (the paper ships no code) and |
| joint-space ISR is an extension beyond the paper, which operates on end-effector positions. |
|
|
| ## Use |
|
|
| ```bash |
| huggingface-cli download angkul07/so101-smolvla-data --repo-type dataset --local-dir . |
| tar -xf std_mm_teleop_v30.tar && tar -xf ego_v30.tar |
| ``` |
|
|
| ```python |
| from lerobot.datasets.lerobot_dataset import LeRobotDataset |
| ds = LeRobotDataset("angkul07/so101-smolvla-data", root="std_mm_teleop_v30") |
| ``` |
|
|
