---
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")
```