docs/quality.md

Data quality

A small fraction of frames contain known sensor artifacts. The repo ships ../bad_frames.json as a skip-list so downstream code can avoid sampling on top of these intervals — important 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.

All frame indices in bad_frames.json are in TRIMMED .pt coordinates. The original H5 recordings have not been edited; only the published .pt files have had their OT-uninitialized prefixes cut (see caveats.md §OT track loss). The per-file trim offset is stored in _contact_meta.trim_offset inside each .pt.

Headline numbers

Total synchronized frames (post-trim)	190,231 (105.7 min @ 30 Hz)
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	56	0.029 %	5 / 27	1–2 frames of uniform pink/magenta wash across the gel surface; adjacent frames normal.	intensity_spike_overview.png
2	OptiTrack pose teleport	56	0.029 %	3 / 27	Translation velocity > 5 m/s or angular velocity > 15 rad/s in a single OT sample (solver flip — physically impossible for human-hand motion).	pose_teleport_samples/
3	OptiTrack track loss	1,680	0.883 %	6 / 27	Sensor pose held at a stale value for ≥ 0.25 s; cross-modal motion check confirms hand was actually moving. Cause: marker briefly left the mocap volume / camera FOV.	freeze_diagnose/

Union of modes 1+2+3: 1,768 / 190,231 = 0.929 % of post-trim frames flagged. Mode 3 is by far the largest — but it's already orders of magnitude smaller than what the raw recordings looked like (15 % of frames before the OT-uninitialized prefixes were trimmed). The remaining 0.88 % is genuine mid-episode mocap dropout that no amount of post-processing can recover.

How to use the data — three recipes

A. Easiest: the shipped example dataloader

ReactWindowDataset does the right thing out of the box. Just turn skip_bad_frames=True:

from examples.react_window_dataset import ReactWindowDataset
from torch.utils.data import DataLoader

ds = ReactWindowDataset(
    data_root       = "processed/mode1_v1/motherboard",
    bad_frames_path = "bad_frames.json",
    tasks_json_path = "tasks.json",
    window_length   = 16,
    skip_bad_frames = True,    # ← drops windows touching ANY of modes 1/2/3
    respect_active_sensors = True,
)

Any window whose [t_start, t_end] range overlaps an intensity_spikes / pose_teleports_{L,R} / ot_loss_{L,R} interval is silently dropped at window-enumeration time, so your DataLoader never sees them.

B. Loading a single .pt directly — DIY skip-list

If you're rolling your own sampler or scanning a single file:

import json, torch

ep_key = "2026-05-11/episode_017"
ep = torch.load(f"processed/mode1_v1/motherboard/{ep_key}.pt", weights_only=False)
T = ep["view"].shape[0]
trim_offset = ep["_contact_meta"].get("trim_offset", 0)   # already applied to ep
bad = json.load(open("bad_frames.json"))["episodes"][ep_key]

# Build a per-frame boolean mask (True = drop)
import numpy as np
mask = np.zeros(T, dtype=bool)
for s_, e_ in (bad["intensity_spikes"]
               + bad["pose_teleports_L"] + bad["pose_teleports_R"]
               + bad["ot_loss_L"] + bad["ot_loss_R"]):
    mask[s_:e_ + 1] = True
print(f"{mask.sum()}/{T} flagged ({100 * mask.mean():.2f} %)")

# Then sample only clean windows:
def clean_window_start(t_start, win_len):
    return not mask[t_start:t_start + win_len].any()

C. Only care about action labels (pose)? Skip just ot_loss

If your model treats vision + tactile as observations and sensor pose as the action label (UMI-style imitation learning), the LED flicker and translation teleports are observation-side noise that you can usually tolerate (single frames or short < 0.3 s spikes). The OT track losses are the one mode you must exclude, because the recorded "action" is stale:

# Just the action-label cuts
def clean_pose_window(t_start, win_len, bad_ep):
    for s_, e_ in (bad_ep["ot_loss_L"] + bad_ep["ot_loss_R"]
                   + bad_ep["pose_teleports_L"] + bad_ep["pose_teleports_R"]):
        if s_ <= t_start + win_len - 1 and e_ >= t_start:
            return False
    return True

What NOT to do

• Don't ignore bad_frames.json — for ot_loss intervals the recorded pose looks plausible (it's just a stale held value), but the actual sensor was moving. A model trained on those will learn that contact-rich tactile signals are uncorrelated with motion.
• Don't try to skip individual frames and stitch the rest — windows must be contiguous over time. Drop the whole window, not just the bad frames in it.

Inspection figures

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

Per-file close-ups: figures/dataset_figures/intensity_spike_samples/.

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

Mode 3 — OptiTrack track loss. 15 sample clips (recording-viewer layout, 30 fps real time, red ring on the frozen-sensor projected dot): freeze_diagnose/ot_loss/.

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,
  "tau_angular_rad_per_s": 15.0,
  "tau_opt_gap_s": 0.10,
  "freeze_threshold_s": 0.25,
  "buffer_frames": 3,
  "summary": {
    "n_episodes": 27,
    "total_frames": 190231,
    "total_bad_frames": 1768,
    "bad_fraction_overall": 0.009294,
    "n_episodes_with_bad_frames": 11
  },
  "episodes": {
    "2026-05-11/episode_003": {
      "n_frames": 10032,
      "duration_s": 334.4,
      "intensity_spikes": [[260, 268], "..."],
      "pose_teleports_L": [],
      "pose_teleports_R": [],
      "ot_loss_L": [],
      "ot_loss_R": [],
      "total_bad_frames": 19,
      "bad_fraction": 0.0019
    }
  }
}

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

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.