Datasets:

LogosRoboticsLab
/

roboreel

human_demos/
  {pose_defined,with_distraction,no_distraction,excluded_human_demos_with_distraction}/<task>/<episode>/
    depths.h5
    egocentric.mp4  front.mp4  left.mp4  right.mp4
robot_trajectories/
  {with_distraction,no_distraction}/<task>/<episode>/
    <timestamp>.h5
    <timestamp>.json
    0.mp4  base_camera.mp4  hand_camera.mp4  left_camera.mp4  overhead_camera.mp4  right_camera.mp4

Episode counts

Task	`human_demos/ with_distraction`	`human_demos/ no_distraction`	`human_demos/ pose_defined`	`robot_trajectories/` (each subset)
`bowl_in_plate`	100	100	100	100
`close_drawer`	100	100	50	100
`empty_basket`	100	100	100	100
`open_lid`	100	100	100	100
`pick_cube`	100	100	110	100
`press_toaster`	100	100	100	100
`push_cube`	100	100	100	100
`stack_cups`	100	100	100	100
`toys_in_basket`	100	100	100	100
`toys_in_drawer`	100	100	100 (as `toy_drawer_close`)	100

human_demos/pose_defined/push_cube contains the same recordings as human_demos/no_distraction/push_cube.

human_demos/excluded_human_demos_with_distraction holds 20 further with-distraction episodes per task (200 in total), numbered 0100-0119, that are not part of the 100-episode with_distraction set.

Human demonstrations

RGB video is stored only in the mp4 files (1280x720, 30 fps, H.264). depths.h5 holds the depth video and calibration. It contains one group per camera (egocentric, front, left, right):

Path	Contents
`<cam>/depth/frames`	`uint16` depth, shape `(T, H, W)`: 720x1280 for `egocentric`, 768x1024 for the others
`<cam>/depth/depth_scale`	Metres per depth unit
`<cam>/depth/intrinsics`, `<cam>/rgb/intrinsics`	Camera intrinsics
`<cam>/depth/distortion`, `<cam>/rgb/distortion`	Distortion coefficients; the `model` attribute names the model
`<cam>/extrinsics/depth_to_rgb_R`, `depth_to_rgb_t`	Depth-to-RGB rotation and translation
`<cam>/timestamps`	Per-frame capture time, seconds since the epoch

Files also carry task_name and demo_id attributes. These are recording metadata, so they may differ from the folder names and episode numbers. Use the folder paths as the task and episode labels.

Reading depth

Depth is compressed with Bitshuffle and zstd, so hdf5plugin must be imported before reading:

import hdf5plugin  # registers the compression filter
import h5py

with h5py.File("human_demos/no_distraction/push_cube/0000/depths.h5", "r") as f:
    cam = f["front"]
    depth_m = cam["depth/frames"][0] * cam["depth/depth_scale"][()]
    timestamps = cam["timestamps"][()]

Synchronisation and alignment

Cameras are not frame-aligned, and frame counts differ between cameras in the same episode. Use timestamps to synchronise them.
Depth is not registered to RGB. Their intrinsics differ, and so does the resolution on front, left and right. Use the calibration above to align them.

Robot trajectories

Generated by motion planning in simulation with a Panda arm (panda_wristcam_hd) using pd_joint_pos control. Each .json file records the environment ID and settings for its episode; with_distraction episodes have distract: true.

Each .h5 file has one group, traj_0, including:

Path	Contents
`traj_0/actions`	Actions, shape `(T, 8)`
`traj_0/obs`	State observations, length `T + 1`
`traj_0/env_states/actors/<name>`	Per-object state, length `T + 1`
`traj_0/env_states/articulations/panda_wristcam_hd`	Robot state, length `T + 1`
`traj_0/rewards`	Per-step rewards, length `T`