README.md

metadata

license: other
task_categories:
  - image-segmentation
tags:
  - robotics
  - video
  - foreground-segmentation

RoboMME demonstration-prefix frames with foreground masks

The demonstration prefix of RoboMME episodes -- the video-instruction frames, where info/is_video_demo is True -- together with a binary foreground mask for each frame.

These frames are worth publishing separately because they are missing from the usual export: the pickle conversion that training pipelines consume writes a file only when is_demo is False, so the demo prefix (292k of RoboMME's 769k frames) exists only in the raw HDF5.

Of the 16 tasks, 9 carry a demo prefix and 7 do not. Both are here, kept apart because the frames mean different things:

	tasks	episodes	frames	what the frames are
<Task>/	9	2 each	6,024	the demo prefix only
no_demo_tasks/<Task>/	7	2 each	6,010	whole episodes -- there is no demo portion to cut

With a demo: InsertPeg, MoveCube, PatternLock, RouteStick, VideoPlaceButton, VideoPlaceOrder, VideoRepick, VideoUnmask, VideoUnmaskSwap. Demo length splits by family -- the Video* tasks run long (VideoPlaceOrder 940 and 1105 frames) while others are short (PatternLock 48 and 63).

Without: BinFill, ButtonUnmask, ButtonUnmaskSwap, PickHighlight, PickXtimes, StopCube, SwingXtimes.

Contents

<Task>/episode_<NNNN>/rgb/00000.png    front camera, 256x256 RGB, raw pixels
<Task>/episode_<NNNN>/mask/00000.png   1-bit foreground mask, same size
<Task>/episode_<NNNN>/done.json        kind, has_demo, demo_len, num_timesteps,
                                       per-frame foreground fraction
no_demo_tasks/<Task>/episode_<NNNN>/   same layout, whole episodes
demo_manifest.json                     episode list for the demo tasks
no_demo_manifest.json                  episode list for the demo-less tasks

Frame 00000 is absolute timestep 0 of the episode and frames run to demo_len - 1, which is the end of the demo prefix under <Task>/ and the end of the episode under no_demo_tasks/. Check kind (demo / full_episode) in done.json rather than inferring from the path. RGB is untouched: no crop, no resize, no normalisation, so any preprocessing is the consumer's choice.

is_video_demo was verified to be a clean prefix -- True..True then False..False -- by reading every timestep of every exported episode.

Masks

ZhengPeng7/BiRefNet (the full model), run at 1024x1024 and thresholded at 0.5, then resized back to 256. The resolution matters: below roughly 512 the mask comes back essentially empty, which makes a foreground-weighted loss put most of its weight on the wooden table instead of the arm and the objects.

The masks are model output, not ground truth. They are accurate on the arm and the manipulated objects and they do pick up small painted markers, but they are not a substitute for simulator segmentation.

These masks were regenerated with the full BiRefNet, replacing an earlier set from BiRefNet_lite. Same resolution, same 0.5 threshold, same resize back -- only the checkpoint changed. The lite model drops the robot arm entirely on some frames: over the 12,034 frames here the full model finds more foreground in 58.2% of them and changes the mask by more than 2% of the frame in 5.5%, and the worst case (VideoPlaceButton episode_0000 frame 00304) goes from a foreground fraction of 0.032 to 0.186 because lite masked the buttons and the cube but not the arm holding them. Mean foreground fraction rose from 0.0795 to 0.0845; per task the shift ranges from -0.0002 (PatternLock) to +0.0176 (BinFill).

done.json's foreground_fraction, seg_model and seg_input_size were recomputed to match.

Provenance and licence

Frames are derived from the RoboMME dataset; its licence governs their use. The masks are generated by BiRefNet_lite (MIT). Nothing here is human-annotated.