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) 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) 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

huggingface-cli download angkul07/so101-smolvla-data --repo-type dataset --local-dir .
tar -xf std_mm_teleop_v30.tar && tar -xf ego_v30.tar

from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("angkul07/so101-smolvla-data", root="std_mm_teleop_v30")