docs/quality.md

Data quality

A small fraction of frames contain known sensor artifacts. The repo ships a ../bad_frames.json index so downstream code can avoid sampling on top of these intervals — useful since this dataset is intended for short-window dynamics / world-model learning, where a glitch landing inside a training window can dominate the loss for that step.

Headline numbers

Total synchronized frames	190,231 (105.7 min @ 30 Hz, post-trim)
Recording files	27
Frames flagged in bad_frames.json	1,768 (0.929 %)
Recording files with ≥1 flagged frame	11

Failure modes — quantitative breakdown

#	Mode	Frames	% of dataset	Files	Symptom	Example
1	GelSight LED flicker	66	0.030 %	5 / 27	1–2 frames of uniform pink/magenta wash across the gel surface; adjacent frames normal. 2026-05-11 / {003, 007, 008, 011, 017}.	intensity_spike_overview.png
2	OptiTrack pose teleport (>5 m/s)	64	0.029 %	3 / 27	Position jumps by >5 cm in 33 ms — mocap lost lock and reacquired. 2026-05-10/{001, 002}, 2026-05-11/015.	pose_teleport_samples/
3	OptiTrack track loss	33,056	14.92 %	8 / 27	OptiTrack lost the rigid body — the recorder kept emitting the held pose. Boundary glitches (ang vel up to 51 k rad/s) appear on re-acquire. Worst single outage: 10.7 min (ep_017). Cause: marker near mocap-volume / camera-FOV boundary. GelSight + RealSense streams are not affected.	freeze_diagnose/

Modes 1+2+3 together: 33,144 / 221,621 = 14.96% of frames flagged. Earlier note: 122 / 221621 = 0.055 % of frames are corrupted in any modality.** Mode 3 is tracked separately because it is healthy data, just non-informative for dynamics. bad_frames.json covers modes 1 and 2 only; if you also want to skip rest periods, intersect with the velocity track from sensor_*_pose.

Inspection figures

Mode 1 — GelSight LED flicker (overview across all 5 affected files):

Per-file close-ups (reference frame, ±1 s neighbors, peak frame): figures/dataset_figures/intensity_spike_samples/.

Mode 2 — OptiTrack pose teleport. GIFs (10 s playback at 2× speed) for all 3 affected files: pose_teleport_samples/.

Modes 1+2+3 — top-6 worst-offenders chart (tactile intensity + sensor velocity time-series):

bad_frames.json schema

Frame indices are inclusive on both ends and pre-padded by buffer_frames (3) on each side so context windows don't bleed into the glitch:

{
  "tau_intensity": 30.0,
  "tau_velocity_mps": 5.0,
  "buffer_frames": 3,
  "summary": {
    "n_episodes": 27,
    "total_frames": 221621,
    "total_bad_frames": 122,
    "bad_fraction_overall": 0.00055,
    "n_episodes_with_bad_frames": 8
  },
  "episodes": {
    "2026-05-11/episode_003": {
      "n_frames": 10032,
      "duration_s": 338.2,
      "intensity_spikes": [[260, 268], "..."],
      "pose_teleports_L": [],
      "pose_teleports_R": [],
      "total_bad_frames": 19,
      "bad_fraction": 0.0019
    }
  }
}

A per-mode aggregate is also available: figures/dataset_figures/data_quality_breakdown.json.

Use in a dataloader

import json
from pathlib import Path

with open("bad_frames.json") as f:
    bad = json.load(f)["episodes"]
with open("tasks.json") as f:
    sessions = json.load(f)["tasks"]["motherboard"]["per_date_notes"]

def is_clean_window(ep_name, t_start, t_end):
    """Return True if [t_start, t_end] does not overlap any flagged interval."""
    intervals = (bad[ep_name]["intensity_spikes"]
                 + bad[ep_name]["pose_teleports_L"]
                 + bad[ep_name]["pose_teleports_R"])
    for s, e in intervals:
        if s <= t_end and e >= t_start:
            return False
    return True

def active_sensors(ep_name):
    """Returns e.g. ['right'] or ['left', 'right']."""
    date = ep_name.split("/")[0]
    return sessions[date]["active_sensors"]

# In your sampler:
#   if not is_clean_window(...): resample
#   sides = active_sensors(...)   # mask out inactive tactile + pose modalities

Full report (per-file CSV)

Every file's individual stats (frames, duration, contact %, max intensity left/right, drift, max pose velocity, etc.) are in figures/dataset_figures/data_quality_report.csv.