Search is not available for this dataset
observation.state listlengths 14 14 ⌀ | action listlengths 14 14 ⌀ | timestamp float32 0 61.4 ⌀ | frame_index int64 0 1.54k ⌀ | episode_index int64 0 3.5k ⌀ | index int64 0 1.86M ⌀ | task_index int64 0 3.41k ⌀ |
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End of preview. Expand in Data Studio
RoboDojo Long-Horizon subtask dataset
The eight tasks of RoboDojo's Long-Horizon capability dimension (the benchmark's own
grouping, scripts/internal/summarize_result.py:DIMENSIONS), 100 demonstration episodes each,
segmented into subtasks and rendered as training rows for a VLM planner.
Layout
| path | what |
|---|---|
frames/<task>/epNNNN/FFFFFF.jpg |
head camera, 512x384, q32 JPEG. FFFFFF is the simulator step index. |
rows/local_sttp.jsonl |
Local-STTP formulation. The prompt carries the subtask the controller has already accepted; the answer is a 1/0 transition flag, a newline, then JSON with current_subtask and keyframe_positions. |
rows/memer.jsonl |
MemER formulation over the same frames and the same call grid: no accepted subtask in the prompt, therefore no flag and no verdict. |
labels/all_tasks_subtasks.jsonl |
the segmentation the rows were built from: per episode, each subtask's span and sentence. |
labels/object_points.json |
per-task object pixel points. |
labels/goals.json |
the global task sentence per task. |
Image paths inside the rows are relative to the archive root, and every one of them has been
checked to resolve against frames/ before upload.
The coordinate convention, which is easy to get wrong
A sentence's at <y, x> is the gripper TCP at the instant of the grasp, in pixels on the
480x640 head frame. It is not the object's centre: the two differ by roughly 20 px in y
(measured 16.6-20.6 px across these tasks). A model trained to point at the object will send a
policy short of where the gripper has to close.
play_tic_tac_toe's coordinate is a destination cell and make_kong's is a push contact, so
neither is a grasp point and neither carries that offset.
Rows per task
| task | rows |
|---|---|
| classify_objects | 4,499 |
| play_tic_tac_toe | 5,637 |
| make_kong | 2,470 |
| put_bottles_into_dustbin | 2,760 |
| organize_table | 3,973 |
| fill_pen_holder | 4,604 |
| fill_egg_holder | 2,759 |
| play_stacking_toy | 5,933 |
| total | 32,635 |
Known coverage gap
fill_egg_holder's success condition includes the holder's lid being closed. Closing the lid
does not close a gripper, so the grip-based segmenter produced no subtask for it and no row here
teaches it. A planner trained on this data cannot complete that task, however well it places the
four eggs.
Unpacking
Frames ship as one gzipped tar per task (88,691 files stall the hub's per-file commit path).
Untar them at the archive root and the relative paths inside rows/*.jsonl resolve as-is:
for f in frames/*.tar.gz; do tar -xzf "$f"; done
# -> frames/<task>/epNNNN/FFFFFF.jpg
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