Lead with robot-arm-free / human-hand collection — README hero + 'What's different' section, tasks.json gets operator/embodiment fields, recording.md explains setup, F7 adds Operator column

README.md

@@ -12,6 +12,7 @@ tags:
   - motion-capture
   - dynamics
   - world-model
+  - human-collected
 pretty_name: React (Tactile-Visual Manipulation)
 size_categories:
   - 100K<n<1M
@@ -28,27 +29,37 @@ configs:
 
 # React
 
-Dense, contact-rich, synchronized multimodal interaction recordings for **tactile-visual dynamics / world-model learning** — *not* a policy / demonstration dataset.
+Dense, contact-rich, synchronized multimodal interaction data collected from **human hands holding handheld GelSight tactile sensors — no robot arm involved**. Intended for **tactile-visual dynamics / world-model learning**, *not* a policy / demonstration dataset.
 
 ![Tactile intensity timeline](figures/contact_intensity_full.png)
 
-> **138 min of synchronized multimodal interaction · 88 min (66 %) of confirmed bimanual tactile contact · 4,136 distinct contact events · 240 k frames @ 30 Hz across 3 × RGB-D + 2 × GelSight + 3-body OptiTrack**
+> **138 min of robot-free human-hand multimodal interaction · 88 min (66 %) of confirmed bimanual tactile contact · 4,136 distinct contact events · 240 k frames @ 30 Hz across 3 × RGB-D + 2 × GelSight + 3-body OptiTrack**
+
+## What's different about this dataset
+
+| | |
+|---|---|
+| **Robot-arm-free** | Recorded directly from a human operator holding two GelSight Mini sensors. No robot kinematics, no embodiment bias, no robot occluding the scene. |
+| **Tactile + RGB-D + mocap, simultaneous** | Most manipulation datasets ship one of these. React ships all three, synchronized to a common 30 Hz clock. |
+| **Contact-dense** | 66 % of all frames have confirmed tactile contact on at least one sensor — see [`figures/contact_intensity_full.png`](figures/contact_intensity_full.png). |
+| **Long, continuous interaction** | Recordings are minutes long, not seconds. Median recording duration is 4 min; longest 19 min. Good for short-window sampling of dynamics, not for action-conditioned policy learning. |
+
+![Comparison with other manipulation datasets](figures/dataset_figures/F7_comparison_table.png)
 
 ## At a glance
 
 | | |
 |---|---|
-| Intended use | Dynamics / world-model learning over short multimodal windows. Sample short trajectories (1 s – 10 s); episode boundaries are not action boundaries. |
+| Embodiment | **Human hands (no robot)** — handheld GelSight sensors with motion-capture rigid bodies |
+| Intended use | Dynamics / world-model learning over short multimodal windows. Sample short trajectories (1 s – 10 s); recording-file boundaries are not action boundaries. |
 | Total synchronized duration | **138.4 min** at 30 Hz (239,759 multimodal frames) |
 | Bimanual tactile-contact time | **87.9 min — 66 % of frames** (4,136 contact events, median 0.73 s) |
 | Cameras | 3× Intel RealSense D415 (color + depth), 480×640, 30 FPS |
-| Tactile | 2× GelSight Mini (left, right) |
+| Tactile | 2× GelSight Mini (left, right), handheld |
 | Motion capture | OptiTrack VRPN, 3 rigid bodies, ~120 Hz |
 | Tasks | `motherboard` (more coming) |
 | License | CC-BY-4.0 |
 
-![Comparison with other manipulation datasets](figures/dataset_figures/F7_comparison_table.png)
-
 ## Recording sessions
 
 | Date | Kind | Active sensors | Notes |
@@ -67,7 +78,7 @@ from datasets import load_dataset
 ds = load_dataset("yxma/React", "motherboard", split="train")
 ```
 
-Or grab a single episode file directly:
+Or grab a single recording file directly:
 
 ```python
 import torch
@@ -93,12 +104,12 @@ import json
 with open("bad_frames.json") as f:
     bad = json.load(f)["episodes"]
 # Drop ~0.085 % of frames flagged in bad_frames.json — see docs/quality.md
-# For 2026-03-23 episodes, also ignore the left-sensor fields (right-only pilot).
+# For 2026-03-23 recordings, also ignore the left-sensor fields (right-only pilot).
 ```
 
-## Episode-file previews
+## Recording-file previews
 
-Per-file GIF previews live under [`figures/episode_previews/`](figures/episode_previews) — first 2 minutes at 10× speed, showing all 3 RealSense cameras with projected GelSight axes plus both tactile pads. (The on-disk recording unit is called an "episode" purely for file naming — episode boundaries don't carry semantic meaning for this dataset.)
+Per-file GIF previews live under [`figures/episode_previews/`](figures/episode_previews) — first 2 minutes at 10× speed, showing all 3 RealSense cameras with projected GelSight axes plus both tactile pads. (The on-disk recording unit is called an "episode" purely for file naming — these boundaries don't carry semantic / action meaning for this dataset.)
 
 ## Repository layout
 
@@ -115,7 +126,7 @@ docs/                                            # extended documentation
 
 | File | Contents |
 |---|---|
-| [`docs/recording.md`](docs/recording.md) | Hardware setup, camera serials, sensor + mocap layout |
+| [`docs/recording.md`](docs/recording.md) | Hardware setup, camera serials, sensor + mocap layout, robot-free collection method |
 | [`docs/schema.md`](docs/schema.md) | Full `.pt` field reference and contact-metric definitions |
 | [`docs/quality.md`](docs/quality.md) | Data-quality breakdown (per-mode), `bad_frames.json` schema, dataloader recipe, inspection figures |
 | [`docs/figures.md`](docs/figures.md) | Dataset statistics + analysis gallery (F1–F8) |

docs/recording.md

@@ -2,13 +2,23 @@
 
 This document describes the hardware and sensor configuration used to record the React dataset. Schema for the resulting `.pt` files is in [`schema.md`](schema.md).
 
+## Collection method — robot-arm-free, human-hand operated
+
+React is recorded **without any robot arm**. A human operator holds one GelSight Mini sensor in each hand and performs the task directly. Compared to robot-collected manipulation data this means:
+
+- No robot kinematic constraints, no robot occluding the scene, no robot-specific visual features.
+- The tactile sensors move with natural human-hand dynamics — varied trajectories, jerks, regrasps, etc.
+- Cross-embodiment by design: any downstream policy / dynamics model trained on this data does not bake in a particular robot morphology.
+
+The trade-off: there are no robot joint angles or commanded actions in the data. Sensor 6-DoF pose comes from a motion-capture system (OptiTrack VRPN) tracking rigid-body markers rigidly attached to the back of each GelSight Mini, and a separate rigid body tracks the manipulated object (e.g. the motherboard). The dataset therefore does **not** include action labels and is **not** an imitation-learning / behavioral-cloning corpus.
+
 ## Sensors
 
 | Stream | Hardware | Native shape | Rate |
 |---|---|---|---|
 | 3 × RealSense color | Intel D415 (serials `143322063538`, `104122062574`, `217222066989`) | 480×640×3 uint8 (BGR) | 30 FPS |
 | 3 × RealSense depth | same | 480×640 uint16 (mm) | 30 FPS |
-| 2 × GelSight tactile | GelSight Mini (left / right) | 480×640×3 uint8 | ~25 FPS, resampled to camera ticks |
+| 2 × GelSight tactile | GelSight Mini (left / right, **handheld**) | 480×640×3 uint8 | ~25 FPS, resampled to camera ticks |
 | 3 × OptiTrack rigid bodies | `motherboard`, `sensor_left`, `sensor_right` | 7-vector (x, y, z, qx, qy, qz, qw) | ~120 Hz |
 
 Each RealSense camera's calibration is stored in the source repo under `twm/calibration/result/T_mocap_to_cam_{middle,left,right}.json`. The serial→position mapping:
@@ -19,7 +29,7 @@ Each RealSense camera's calibration is stored in the source repo under `twm/cali
 | `cam1` | `104122062574` | `T_mocap_to_cam_left.json`  | left-side view |
 | `cam2` | `217222066989` | `T_mocap_to_cam_middle.json`| overhead / center view |
 
-The OptiTrack-to-GelSight rigid transforms are stored alongside in `T_gel_to_rigid_{left,right}.json`. These let you project the GelSight contact center into any of the three camera views; see the per-episode GIFs under `figures/episode_previews/` for examples.
+The OptiTrack-to-GelSight rigid transforms are stored alongside in `T_gel_to_rigid_{left,right}.json`. These let you project the GelSight contact center into any of the three camera views; see the per-recording GIFs under `figures/episode_previews/` for examples.
 
 ## Timing
 

tasks.json

@@ -3,6 +3,8 @@
     "motherboard": {
       "language": "Bimanual manipulation of components on a computer motherboard.",
       "purpose": "Dense, contact-rich, synchronized multimodal interaction data for tactile-visual dynamics / world-model learning. Not a policy-learning / demonstration dataset.",
+      "operator": "human hands (handheld GelSight Mini sensors with motion-capture rigid bodies; no robot arm involved)",
+      "embodiment": "human",
       "dates": ["2026-03-23", "2026-05-10", "2026-05-11"],
       "per_date_notes": {
         "2026-03-23": {