README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - robotics
tags:
  - lerobot
  - egocentric
  - multimodal
  - stereo
  - imu
pretty_name: Robotrain Multi-Camera Sample Dataset
size_categories:
  - 100K<n<1M

Robotrain Multi-Camera Sample Dataset

A multimodal human egocentric observation dataset in LeRobot v3 format. It captures synchronized multi-view video and head motion sensing during workshop material organization and object handling.

About Robotrain

This dataset was collected by Robotrain, a robotics research company.

For more information about Robotrain and our work, visit our website: https://robotrain.ai

What this dataset offers

• Five time-aligned camera views: head RGB, head stereo pair, left wrist RGB, right wrist RGB
• Head 9-axis IMU (accelerometer, gyroscope, magnetometer) plus a fused orientation quaternion, resampled to the video clock
• Published stereo intrinsics, distortion, extrinsics and baseline for metric depth reconstruction from the stereo pair
• A measured cross-camera alignment bound (maximum skew 16.69 ms, within one frame at 30 fps)
• Official LeRobot v3 layout with statistics suitable for normalization and dataset surgery
• Anonymous multi-session indices for session-aware splits

The release contains observations only (no robot actions or robot state). It is intended for representation learning, cross-view learning, perception, stereo/depth research and related pretraining.

Recorder/participant permission and venue authorization for this release have been obtained. Licensed under CC BY-NC 4.0 (noncommercial use with attribution).

---

Dataset summary

Metric	Value
Total episodes	213
Total frames	189,823
Frame rate	30 fps
Total duration	105.5 minutes (1 h 45.5 min)
Recording sessions	3 anonymous (session_index 0-2)
Video streams	5 synchronized cameras
Sensor modalities	Head IMU (9) + orientation quaternion (4)
Unique tasks	1
Cross-camera alignment	< 1 frame at 30 fps (measured max skew 16.69 ms)
Video data size	~20.1 GB
Sensor data size	~5.9 MB (Parquet)
Typical / median episode	30.0 s (900 frames)
Shortest episode	7.3 s (219 frames)
Longest episode	30.0 s (900 frames)

By session

session_index	Episodes	Duration frames	Episode index range
0	132	118,132	0-131
1	15	12,972	132-146
2	66	58,719	147-212

By task

Task	Episodes	Frames
organizing workshop materials and handling objects	213	189,823

Three episodes (indices 131, 146, 212) are shorter than the 30 s target; they are session-boundary tails retained after unlabeled-frame curation.

---

File structure

dataset/
|__ README.md
|__ LICENSE
|__ meta/
|   |__ info.json                     # schema, features, configuration
|   |__ stats.json                    # per-feature statistics
|   |__ tasks.parquet                 # task label table
|   |__ stereo_calibration.json       # stereo intrinsics / extrinsics
|   |__ publication_manifest.json
|   |__ verification.json
|   |__ episodes/chunk-000/file-000.parquet
|__ data/chunk-000/file-000.parquet   # all sensor rows
|__ videos/
    |__ observation.images.egocentric/chunk-000/file-{000-212}.mp4
    |__ observation.images.stereo_left/chunk-000/file-{000-212}.mp4
    |__ observation.images.stereo_right/chunk-000/file-{000-212}.mp4
    |__ observation.images.wrist_left/chunk-000/file-{000-212}.mp4
    |__ observation.images.wrist_right/chunk-000/file-{000-212}.mp4

Each video file corresponds to one episode. Episode N maps to file-{N:03d}.mp4 across all camera streams.

---

Modalities

1. Video streams (5 cameras)

All videos are H.264, 30 fps, yuv420p, with no audio.

Stream	Resolution	Mounting / role
observation.images.egocentric	1280x800	Head-mounted first-person RGB
observation.images.stereo_left	640x400	Head-mounted stereo left (3-channel container)
observation.images.stereo_right	640x400	Head-mounted stereo right (3-channel container)
observation.images.wrist_left	1920x1080	Left wrist / forearm RGB
observation.images.wrist_right	1920x1080	Right wrist / forearm RGB

2. Head IMU

Feature	Shape	Channels	Placement
observation.imu.head	(9,)	accel(3) + gyro(3) + mag(3)	Head / camera frame
observation.imu.head_quat	(4,)	qi, qj, qk, qreal	Fused orientation

IMU streams are resampled to 30 fps to align with video frames.

3. Temporal alignment

All camera streams are synchronized to a shared 30 fps frame clock. Measured maximum cross-camera skew for this release is 16.69 ms (less than one frame). Variable-rate IMU samples are held to the nearest video frame.

4. Stereo calibration and depth

meta/stereo_calibration.json describes the shipped stereo pixels (640x400). Videos are not pre-rectified.

Left  fx=284.19  fy=284.14  cx=323.84  cy=197.25
Right fx=285.46  fy=285.49  cx=316.32  cy=197.73
Distortion model: rational_polynomial_14 (14 coefficients per camera)
Baseline: 75.183 mm
left->right translation_mm ≈ [-75.17, 0.24, -1.10]

After rectification and stereo matching:

depth_mm ≈ (fx * baseline_mm) / disparity

Use the published matrices as-is for these frames.

import json
from pathlib import Path

import cv2
import numpy as np

root = Path("/path/to/dataset")
calib = json.loads((root / "meta/stereo_calibration.json").read_text())

K1 = np.asarray(calib["stereo_left"]["intrinsic_matrix"], dtype=np.float64)
K2 = np.asarray(calib["stereo_right"]["intrinsic_matrix"], dtype=np.float64)
D1 = np.asarray(calib["stereo_left"]["distortion_coefficients"], dtype=np.float64)
D2 = np.asarray(calib["stereo_right"]["distortion_coefficients"], dtype=np.float64)
R = np.asarray(calib["left_to_right_rotation"], dtype=np.float64)
T = np.asarray(calib["left_to_right_translation_mm"], dtype=np.float64).reshape(3, 1)
image_size = (calib["stereo_left"]["width"], calib["stereo_left"]["height"])

R1, R2, P1, P2, Q, _, _ = cv2.stereoRectify(
    K1, D1, K2, D2, image_size, R, T, flags=cv2.CALIB_ZERO_DISPARITY, alpha=0
)
print("baseline_mm", float(calib["baseline_mm"]))
print("Q shape", Q.shape)

---

Loading the dataset

Prerequisites

pip install lerobot pandas pyarrow opencv-python-headless

Official LeRobot loader

from pathlib import Path

from lerobot.datasets.lerobot_dataset import LeRobotDataset

root = Path("/path/to/dataset")
ds = LeRobotDataset(repo_id="local/Robotrain-multi-cam-sample", root=root)

print(len(ds), ds.meta.total_episodes, ds.fps)
sample = ds[0]
print(sample["observation.images.egocentric"].shape)  # (3, 800, 1280)
print(sample["observation.imu.head"].shape)           # (9,)
print(sample["task"])

Parquet + video paths

import json
from pathlib import Path

import numpy as np
import pandas as pd

root = Path("/path/to/dataset")
info = json.loads((root / "meta/info.json").read_text())
tasks = pd.read_parquet(root / "meta/tasks.parquet")
episodes = pd.read_parquet(root / "meta/episodes/chunk-000/file-000.parquet")
data = pd.read_parquet(root / "data/chunk-000/file-000.parquet")

episode_id = 0
ep = data[data["episode_index"] == episode_id].reset_index(drop=True)
imu = np.stack(ep["observation.imu.head"].to_numpy())
quat = np.stack(ep["observation.imu.head_quat"].to_numpy())

video = root / "videos/observation.images.egocentric/chunk-000/file-000.mp4"
print(info["total_frames"], len(episodes), list(tasks.index), imu.shape, video.is_file())

---

Feature reference

Feature	Type	Shape	Description
observation.images.egocentric	video	1280x800x3	Head RGB
observation.images.stereo_left	video	640x400x3	Stereo left
observation.images.stereo_right	video	640x400x3	Stereo right
observation.images.wrist_left	video	1920x1080x3	Left wrist RGB
observation.images.wrist_right	video	1920x1080x3	Right wrist RGB
observation.imu.head	float32	(9,)	accel + gyro + mag
observation.imu.head_quat	float32	(4,)	orientation quaternion
timestamp	float32	(1,)	seconds within episode
frame_index	int64	(1,)	frame within episode
episode_index	int64	(1,)	episode id
index	int64	(1,)	global row index
task_index	int64	(1,)	task id
session_index	int64	(1,)	anonymous session id

Public training features are limited to the table above. Internal timing diagnostics, sensor-confidence flags, source identifiers and unpublished calibration are not part of this release. Standard LeRobot meta/stats.json and per-episode stats/* columns are retained for normalization and surgery.

---

License

Creative Commons Attribution-NonCommercial 4.0 International (cc-by-nc-4.0). You may share and adapt for noncommercial purposes with attribution. Commercial use is not permitted. See LICENSE.