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

Rows are written at 30 Hz, but the tactile stream updates more slowly — see Tactile sampling rate.

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)
tactile_{left,right}_is_new	bool	True when that row is a fresh tactile reading (not a repeat of the previous row)
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.

Tactile sampling rate (read this before training on touch)

Parquet rows and all five videos are written at 30 Hz, but the GelSight stream does not carry 30 Hz of information. Measured across the whole release:

	value
tactile rows	480 080 (2 sensors × 240 k frames)
genuinely distinct tactile frames	135 297
duplicated rows	71.8 %
effective tactile rate	~8.5 fps
longest frozen stretch	30 frames (1.0 s)

Two causes, one fixed:

1. Sensor ceiling — the GelSight Mini streams 3280×2464 MJPG at 18.75 fps. Some duplication against a 30 Hz row clock is unavoidable (~40 %).
2. Recording-side decode backlog (all currently published data) — the rig decoded each full 8 MP frame on the capture thread (~71 ms), so tactile effectively ran at ~8 fps and every frame was reused ~3.6×. Fixed on the rig on 2026-06-27 (reduced-scale decode + per-sensor capture timestamps); recordings from that date on reach the 18.75 fps ceiling.

Use the flags. Every row carries tactile_left_is_new / tactile_right_is_new:

df = pq.read_table("episode_000.parquet").to_pandas()
fresh = df[df.tactile_left_is_new]     # 8.5 fps of real readings

Training tactile dynamics on all rows teaches the model that touch mostly does not change; it does, we just sampled it slowly. Visual and pose streams are unaffected — those are genuinely 30 Hz.

The flags were recovered from the shipped contact metrics (a repeated frame gives a bit-identical metric triple) and checked frame-by-frame against the source recordings: 0 mismatches over 899 frames on each of 7 audited episodes, spanning both tasks. The same check independently recovers the +15-frame latency correction baked into the release.

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.

Runt episodes. Two motherboard recordings are far too short to be complete demonstrations and are best filtered out: 2026-05-19/episode_003 (4.0 s) and 2026-05-19/episode_004 (7.0 s). Median episode length is 213 s; these two are together 0.8 % of the release. They are shipped rather than deleted so episode numbering stays stable.

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.