dataset card: current numbers, statistics figures, and the force/tactile limits

README.md

@@ -36,11 +36,61 @@ configs:
 
 Dense, contact-rich, synchronized multimodal interaction data collected from **human hands holding handheld GelSight tactile sensors** (no robot arm). Intended for **tactile-visual dynamics / world-model learning**.
 
-> **133 min · 240 k frames @ 30 Hz · 3× RGB + 2× GelSight + OptiTrack · 2 tasks**
+> **173 min · 312 k frames @ 30 Hz · 3× RGB + 2× GelSight + 2× wrist + OptiTrack · 3 tasks**
+
+![dataset statistics](assets/stats_wrist_era.png)
+
+*Per-task scale, contact-force distribution, bimanual contact occupancy, and tactile validity for the wrist-camera era. Panel B shows the ECDF of unsaturated contact force; the steps are real — the force calibration ends in an isotonic (piecewise-constant) stage, so its output is quantised. See [Known limits](#known-limits).*
+
+## What is published
+
+Episodes are **cut into clean segments** before release: every span a defect
+detector flagged is removed, and each remaining span of at least 30 s is
+published as its own episode. So the flagged frames are the **gaps between**
+published episodes, and you do not need to consult `bad_frames.json` before
+training on `data/<task>/`.
+
+| Task | Source recordings | Published segments | Duration | Median contact force |
+|---|---|---|---|---|
+| **motherboard** | 12 | 25 | 93.9 min | 2.94 N |
+| **pushT** | 10 | 31 | 32.5 min | 4.29 N |
+| **rope** | 10 | 27 | 47.1 min | 1.38 N |
+| *(total)* | *32* | *83* | *173.5 min* | |
+
+`data/validation/` holds a separate 5-segment / 7.1 min set from an
+earlier session (2026-09-09) that carries **different wrist cameras and a
+different calibration epoch**. It is not a random held-out split — training on
+`data/<task>` and evaluating there measures domain shift. See its own README.
+
+![earlier session](assets/stats_arducam_session.png)
+
+## Known limits
+
+Read these before using the force channel or the tactile stream.
+
+**Force saturates at 7.87 N.** The calibration was fitted on presses up to
+8 N, and its final stage is an isotonic regression, which cannot extrapolate —
+anything above the fitted range is clipped to 7.87 N. Affected fraction of
+**contact** frames:
+- `motherboard` — 8.3 %
+- `pushT` — 8.4 %
+- `rope` — 0.6 %
+
+Treat those frames as **right-censored**, not as measurements. The force labels
+also carry roughly **1 N of held-out error**, so do not compare models at 0.1 N
+resolution.
+
+**Tactile frames repeat.** The GelSight Mini tops out near 17.8 Hz while rows
+are written at 29.8 Hz, so only about **56–58 %** of rows are a fresh sensor
+reading. `tactile_{left,right}_is_new` marks which. Train tactile dynamics on
+the flagged rows, not on all of them.
+
+**`duration_s` is `n_frames / 30`**, the nominal write tick. The RealSense
+streams actually run at 29.80 Hz.
 
 ## Format — LeRobot-style video release
 
-Each episode ships as **5 MP4 video streams** (640×480, H.264) + a **per-frame parquet** of poses and contact metrics. This matches how LeRobot / DROID / Open X-Embodiment ship manipulation data: tiny on disk (whole dataset ≈ 4.3 GB vs ~1 TB raw), random-access decodable, training-ready.
+Each episode ships as **7 MP4 video streams** (640×480, H.264) + a **per-frame parquet** of poses and contact metrics. This matches how LeRobot / DROID / Open X-Embodiment ship manipulation data: tiny on disk (data/ ≈ 21 GB vs ~1.9 TB raw), random-access decodable, training-ready.
 
 ```
 data/<task>/
@@ -50,7 +100,8 @@ data/<task>/
 │   └── calibration.json             # epoch, applies-to dates, RMSE, chain
 ├── videos/<date>/episode_NNN/
 │   ├── view_left.mp4  view_middle.mp4  view_right.mp4    # 640×480 RGB
-│   └── tactile_left.mp4  tactile_right.mp4               # 640×480 GelSight
+│   ├── tactile_left.mp4  tactile_right.mp4               # 640×480 GelSight
+│   └── wrist_left.mp4  wrist_right.mp4                   # 640×480 wrist cams
 ├── meta/<date>/episode_NNN.parquet  # one row per frame (see below)
 ├── episodes.jsonl                   # one row per episode
 ├── segments.json                    # clean-segment index (no bad frames)
@@ -63,7 +114,11 @@ data/<task>/
 |---|---|---|
 | `frame_idx` / `frame_index` | int | 0…T-1, matches MP4 frame index |
 | `episode` / `episode_index` | str / int | source episode key and its 0-based index within the task |
-| `task` / `task_index` | str / int | task name and index (0=motherboard, 1=pushT) |
+| `task` / `task_index` | str / int | task name and index (0=motherboard, 1=pushT, 2=rope) |
+| `force_{L,R}_normal_n` | float32 | estimated normal contact force (N) — **censored at 7.87 N**, see Known limits |
+| `force_{L,R}_penetration_mm` | float32 | gel penetration depth (mm) |
+| `force_{L,R}_source_frame` | int | the raw tactile frame this force was computed from |
+| `tactile_{L,R}_is_new` | bool | True when the row is a fresh sensor reading, not a repeat |
 | `timestamp` | float64 | camera clock (s) |
 | `sensor_left_pose`, `sensor_right_pose` | list[7] | OptiTrack world pose of each GelSight (xyz metres + quat **xyzw**, scalar-LAST — `scipy...Rotation.from_quat` takes it as-is) |
 | `object_pose` | list[7] | OptiTrack world pose of the manipulated object (NaN where the object body was not tracked — e.g. all pushT) |
@@ -82,34 +137,38 @@ data/<task>/depth/<date>/episode_NNN/depth_{left,middle,right}.mkv
 - Decode with PyAV (`frame.to_ndarray()` → `(480, 640)` uint16). cv2 cannot read 16-bit video.
 - Load via `ReactVideoDataset(..., load_depth=True)`.
 
-## Tasks
+## Tasks and calibration
 
-| Task | Episodes | Dates | Duration | Clean segments | Calibration |
+| Task | Dates | Source recordings | Published segments | Duration | Calibration epoch |
 |---|---|---|---|---|---|
-| **motherboard** | 32 | 2026-05-10/11/19 | 108 min | 76 (107 min) | **May-12** (RMSE ~5 mm) |
-| **pushT** | 4 | 2026-06-18 | 25 min | 17 (25 min) | **June-26** (RMSE ~0.6 px) |
+| **motherboard** | 2026-09-11, 2026-09-12 | 12 | 25 | 93.9 min | 2026-09-09 |
+| **pushT** | 2026-09-10, 2026-09-11 | 10 | 31 | 32.5 min | 2026-09-09 |
+| **rope** | 2026-09-11 | 10 | 27 | 47.1 min | 2026-09-09 |
+| **validation** | 2026-09-09 | 5 | 9 | 25.2 min | 2026-09-09 |
 
-See [`tasks.json`](tasks.json) for the machine-readable registry (per-task dates, sensors, calibration epoch, world-frame offsets).
+All currently published sessions share the **2026-09-09** extrinsics. Camera
+extrinsics are used only for the projection overlay; **stored poses are
+OptiTrack world-frame** and independent of calibration. Poses are **Z-up**.
 
-### Calibration epochs
-Cameras were **recalibrated between tasks**. Each task points to the calibration valid for its recordings:
-- `motherboard` → **May-12** extrinsics (`data/motherboard/calibration/`)
-- `pushT` → **June-26** extrinsics (`data/pushT/calibration/`)
+`data/validation/` mixes two tasks and two sessions of the same date, and its
+episode numbers are **not** the recorder's — the recorder reuses numbers within
+a date, so every row there carries `source_recording`. Read its README before
+using it.
 
-Camera extrinsics are used only for the projection overlay; **stored poses are OptiTrack world-frame** and independent of calibration. The 2026-05-19 motherboard session had a redefined world origin; an offset `(0.23, 0, 0.175) m` is already baked into its poses so all dates share one frame (recorded in `episodes.jsonl`).
+See [`tasks.json`](tasks.json) for the machine-readable registry.
 
 ## Downloading — depth is optional
 
-The dataset splits into a **lightweight core** (RGB + tactile + poses, ~4.4 GB) and an **optional depth tree** (`data/<task>/depth/`, ~33 GB lossless). Depth lives in its own subtree so you can skip it entirely.
+The dataset splits into a **lightweight core** (RGB + tactile + wrist + poses, ~21 GB) and an **optional depth tree** (`data/validation/depth/`, ~6 GB lossless; only the 2026-09-09 session has depth). Depth lives in its own subtree so you can skip it entirely.
 
 ```python
 from huggingface_hub import snapshot_download
 
-# Core only — RGB + tactile + parquet, NO depth (~4.4 GB)
+# Core only — RGB + tactile + wrist + parquet, NO depth (~21 GB)
 snapshot_download("yxma/React", repo_type="dataset",
                   ignore_patterns=["*/depth/*"])
 
-# Everything including depth (~37 GB)
+# Everything including depth (~27 GB)
 snapshot_download("yxma/React", repo_type="dataset")
 
 # One task only
@@ -161,26 +220,21 @@ The invariant worth knowing: **rotating both changes no picture.** Measured on
 GelSight centre moves 0.000000 px between the two conventions. Newtons,
 timestamps and every video frame are identical either way.
 
-## ⚠️ Known issue: tactile acquisition latency (~15 frames)
+## Tactile acquisition latency — does NOT affect any published episode
 
-Recordings **up to and including 2026-06-18** have a GelSight-vs-camera capture
-lag of **≈15 frames (~0.5 s)**: the tactile stream at index `i` was physically
-captured ~15 frames *before* the camera/pose at the same index. Cause: a
-recording-side `cv2.VideoCapture` V4L2 buffer that was never flushed
-(throttled reads + no `BUFFERSIZE=1` + default pixel format). Fixed in the rig
-on 2026-06-27; **future recordings will not have this lag**.
+Recordings up to and including **2026-06-18** had a GelSight-vs-camera capture
+lag of ≈15 frames (~0.5 s), caused by a recording-side `cv2.VideoCapture` V4L2
+buffer that was never flushed. It was fixed in the rig on **2026-06-27**.
 
-The streams are stored frame-aligned by tick index, so this lag is baked in but
-**correctable**. The reference loader compensates at load time:
+**Every episode currently published was recorded on 2026-09-09 or later**, and
+each one carries per-sensor GelSight timestamps, so the tactile stream is
+resampled onto the camera clock during the build. Verified across all 80
+published force records: `tactile_timestamped` is True for every one, and
+`tactile_align.gel_lag_frames` returns **0**.
 
-```python
-ds = ReactVideoDataset("data/motherboard", tactile_latency=15)  # pairs view[i] with tactile[i+15]
-```
-
-`tactile_latency` shifts both the tactile videos and the tactile contact-scalar
-columns; poses/views/depth are unchanged. Set `tactile_latency=0` for the raw
-(uncompensated) data. The exact per-session value should be re-measured with
-`camera_stream/measure_gelsight_latency.py`.
+**Do not apply a 15-frame shift.** Doing so would introduce the half-second
+misalignment this note used to warn about. The correction is relevant only if
+you obtain one of the withdrawn 2026-05/06 releases.
 
 ## Data quality
 Per-task `bad_frames.json` flags `intensity_spikes`, `pose_teleports_{L,R}`, `ot_loss_{L,R}` (OptiTrack track loss). Overall flagged: motherboard 0.90 %, pushT 0.67 %. `segments.json` already excludes them.
@@ -192,3 +246,4 @@ Per-task `bad_frames.json` flags `intensity_spikes`, `pose_teleports_{L,R}`, `ot
 
 ## License
 [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
+