| --- |
| license: mit |
| --- |
| |
| # RoboReel |
|
|
| Human demonstration videos and simulated robot trajectories for 10 manipulation tasks. |
|
|
| ## Layout |
|
|
| ``` |
| human_demos/ |
| train/{no_distraction,with_distraction,pose_defined}/<task>/<episode>/ |
| eval/{no_distraction,with_distraction}/<task>/<episode>/ |
| 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 |
|
|
| Human demos, per task: |
|
|
| | Task | `train/no_distraction` | `train/with_distraction` | `train/pose_defined` | `eval/no_distraction` | `eval/with_distraction` | |
| |---|---|---|---|---|---| |
| | `bowl_in_plate` | 80 | 80 | 100 | 20 | 20 | |
| | `close_drawer` | 80 | 80 | 50 | 20 | 20 | |
| | `empty_basket` | 80 | 80 | 100 | 20 | 20 | |
| | `open_lid` | 80 | 80 | 100 | 20 | 20 | |
| | `pick_cube` | 80 | 80 | 110 | 20 | 20 | |
| | `press_toaster` | 80 | 80 | 100 | 20 | 20 | |
| | `push_cube` | 80 | 80 | 100 | 20 | 20 | |
| | `stack_cups` | 80 | 80 | 100 | 20 | 20 | |
| | `toys_in_basket` | 80 | 80 | 100 | 20 | 20 | |
| | `toys_in_drawer` | 80 | 80 | 100 (as `toy_drawer_close`) | 20 | 20 | |
|
|
| Robot trajectories: 100 episodes per task in each of `no_distraction` and `with_distraction`. |
|
|
| ## Train and evaluation split |
|
|
| In `no_distraction` and `with_distraction`, episodes `0000`-`0079` are in `train` and episodes |
| `0080`-`0099` are in `eval`. All `pose_defined` demos are used for training only. |
|
|
| `human_demos/train/pose_defined/push_cube` contains the same recordings as `no_distraction/push_cube`, |
| including the episodes in `eval/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`. They are not part of `train` or `eval`. |
|
|
| ## 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: |
|
|
| ```python |
| import hdf5plugin # registers the compression filter |
| import h5py |
| |
| with h5py.File("human_demos/train/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` | |
|
|
