React — Multi-Task Tactile-Visual Manipulation

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

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.

data/<task>/
├── calibration/                     # OptiTrack→camera extrinsics for this task
│   ├── T_mocap_to_cam_{left,middle,right}.json
│   ├── T_gel_to_rigid_{left,right}.json
│   └── 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
├── 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)
├── bad_frames.json                  # quality intervals per episode
└── previews/<date>/episode_NNN.mp4  # 1280×480 viewer-layout preview

parquet columns (per frame, aligned to video frame i)

Column	Type	Meaning
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)
timestamp	float64	camera clock (s)
sensor_left_pose, sensor_right_pose	list[7]	OptiTrack world pose of each GelSight (xyz + quat wxyz)
object_pose	list[7]	OptiTrack world pose of the manipulated object (NaN where the object body was not tracked — e.g. all pushT)
tactile_{L,R}_{intensity,area,mixed}	float32	contact metrics (computed at full 640×480)
source_h5_frame	int	index into the original recording

Decoded frames are RGB (standard decoder convention) for all five RGB streams.

depth (optional, data/<task>/depth/)

Per-camera depth is shipped as lossless FFV1 16-bit video (gray16le):

data/<task>/depth/<date>/episode_NNN/depth_{left,middle,right}.mkv

• uint16, millimeters; 0 = no return / invalid.
• Frame i aligns to the RGB video frame i and parquet row i.
• Decode with PyAV (frame.to_ndarray() → (480, 640) uint16). cv2 cannot read 16-bit video.
• Load via ReactVideoDataset(..., load_depth=True).

Tasks

Task	Episodes	Dates	Duration	Clean segments	Calibration
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)

See tasks.json for the machine-readable registry (per-task dates, sensors, calibration epoch, world-frame offsets).

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/)

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).

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.

from huggingface_hub import snapshot_download

# Core only — RGB + tactile + parquet, NO depth (~4.4 GB)
snapshot_download("yxma/React", repo_type="dataset",
                  ignore_patterns=["*/depth/*"])

# Everything including depth (~37 GB)
snapshot_download("yxma/React", repo_type="dataset")

# One task only
snapshot_download("yxma/React", repo_type="dataset",
                  allow_patterns=["data/motherboard/*"], ignore_patterns=["*/depth/*"])

Or use the helper: python examples/download.py --no-depth (see examples/download.py).

The ReactVideoDataset loader never touches depth unless you pass load_depth=True, so depth-free training requires no depth download.

Loading

from examples.react_video_dataset import ReactVideoDataset

ds = ReactVideoDataset("data/motherboard", window_length=16, mode="segment")
sample = ds[0]
# sample["view_middle"]:       (16, 480, 640, 3) uint8 RGB
# sample["tactile_left"]:      (16, 480, 640, 3) uint8 RGB
# sample["sensor_left_pose"]:  (16, 7) float32

mode="segment" iterates clean spans (no bad frames by construction); mode="window" slides over whole episodes and skips bad_frames.json intervals. Backend: PyAV (install decord for faster random access).

✅ Tactile latency corrected (was ~15 frames)

Recordings up to and including 2026-06-18 HAD 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.

The streams are stored frame-aligned by tick index, so this lag is baked in but now corrected in the published data (tactile shifted +15f, rebuilt from raw H5). No loader flag needed. The loader still accepts tactile_latency= for raw data:

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.

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.

Notes

• Depth is available in the source recordings and will be added under data/<task>/depth/ in a later upload.
• One pushT source recording (episode_004) was corrupt and excluded.
• The previous single-task .pt release (episodes/, segments/) is superseded by this video format.

License

CC-BY-4.0.