license: cc-by-4.0
task_categories:
- robotics
tags:
- robotics
- tactile
- manipulation
- multimodal
- gelsight
- realsense
- motion-capture
- world-model
- human-collected
- lerobot
pretty_name: React (Tactile-Visual Manipulation)
size_categories:
- 100K<n<1M
configs:
- config_name: motherboard
data_files:
- split: train
path: data/motherboard/meta/**/*.parquet
- config_name: pushT
data_files:
- split: train
path: data/pushT/meta/**/*.parquet
- config_name: all
default: true
data_files:
- split: train
path: data/**/meta/**/*.parquet
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 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) |
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
ialigns to the RGB video frameiand parquet rowi. - 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).
World frame — Z-up as published, Y-up on request
Every pose in this release is Z-up: sensor_{left,right}_pose,
object_pose, force_{left,right}_target_pose, and the T_mocap_to_cam
extrinsics in each calibration/. Each subset says so itself, in
episodes.jsonl ("up_axis": "z") and in every calibration file, so nothing
has to infer it from a directory name.
OptiTrack records Y-up. The release is rotated once, by R_x(-90):
(x, y, z) -> (x, -z, y). If you want the raw convention, ask the loader:
ds = ReactVideoDataset("data/motherboard", up_axis="y") # default is "z"
cal = ds.calibration() # comes back in the SAME convention
Take the calibration from the dataset, not from disk. This is a rotation of
the world frame, so poses and T_mocap_to_cam have to move together — one
without the other leaves every projection up to 165 px from the sensor and
raises nothing. react_toolbox.frames is the same conversion if you need it
directly: convert_poses(poses7, to_zup=), as_up_axis(cal, want).
The invariant worth knowing: rotating both changes no picture. Measured on
data/validation over 450 (frame, camera, sensor) combinations, the projected
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)
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.
The streams are stored frame-aligned by tick index, so this lag is baked in but correctable. The reference loader compensates at load time:
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
.ptrelease (episodes/,segments/) is superseded by this video format.