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license: apache-2.0
task_categories:
- robotics
tags:
- robodojo
- annotations
---
# RoboDojo progress annotations
Frame-level labels over the official RoboDojo episodes, for the six tasks that
require remembering something seen earlier in the episode. They carry no images,
states, actions or episodes; the observations and actions a policy trains on
come from [`RoboDojo-Benchmark/Robodojo`](https://huggingface.co/datasets/RoboDojo-Benchmark/Robodojo).
Published so that the training recipe behind our RoboDojo entry can be run by
anyone: without these labels the keyframe-memory stage has nothing to learn
from.
## What is in it
600 files, `<task>/trajectory_<n>/progress.json`, covering all 100 episodes of
each of the six tasks. Plus `index.json`, which maps each task directory to the
official task instruction and states the schema.
| field | meaning |
| --- | --- |
| `keyframes` | the frames at which the task made visible progress that has to be remembered |
| `meta[].frame` / `meta[].box` | `[x1, y1, x2, y2]` in raw-video pixels, the object that progress was about |
| `meta[].window` | `[first, last]` — the frames at which recording that keyframe still counts as correct |
| `total_frames` | the episode's length, so a file can be checked against the episode it describes |
`meta[].window` exists because a keyframe is a moment, not a frame: several
consecutive frames show the same progress, so a selector that answers one frame
early is not wrong. Training scores the answer against the whole interval
rather than against a single index.
## Per-task shape
| task | episodes | keyframes each | boxes each | acceptance intervals each |
| --- | --- | --- | --- | --- |
| `cover_blocks` | 100 | 4 | — | — |
| `imitate_sorting_sequence` | 100 | 5 | 5 | — |
| `match_and_pick_from_conveyor` | 100 | 1 | — | 1 |
| `press_by_number` | 100 | 5–21 | — | — |
| `swap_T` | 100 | 1 | — | — |
| `swap_blocks` | 100 | 7 | — | 1 |
Where a task has no acceptance interval, the selection answer is scored against
the single annotated candidate.
`swap_T` and `match_and_pick_from_conveyor` carry one keyframe, at frame 0: what
has to be remembered is the initial position and orientation of the two blocks,
and the first object on the conveyor. Both are visible at the start.
## Checking them against the official episodes
An annotation bound to the wrong episode would train a policy to recall the
wrong frames. Each file therefore records its episode's length; the loader binds
by task instruction and stops if a length disagrees.
## How they were produced
For each task we decided by hand what counts as a keyframe — the moments at
which the task makes the progress a policy has to remember, such as a block
being covered or a button press being committed. A script then located the
frames that satisfy the rule in each episode, and every file was checked by a
person and corrected where the script had put a frame in the wrong place.
## Using them
```bash
hf download x-square-robot/robodojo-memory-annotations --repo-type dataset \
--local-dir <bench_root>/data/robodojo_memory_annotations
```
The training code that reads them is in
[wall-x](https://github.com/X-Square-Robot/wall-x)
(`wall_x/data/backends/lerobot/robodojo/`); see the WallX policy README in XPolicyLab
for the two training stages.
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