Datasets:
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_lite, 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.
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.
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