render_request/README.md

# Render request — held-out benchmark, 10 tasks × 20 demos

Everything needed is in this folder. Read the attempt lists here, upload the rendered output here.

**What:** re-render 200 existing demonstrations (10 tasks × 20 attempts) for the **default Franka**,
at the **stage1_5k camera rig**.

**Why re-render:** these demonstrations currently exist only at a superseded camera rig. The
pretraining corpus (`stage1_5k_camera_fixed`) uses a different one, and this benchmark evaluates
models pretrained on it — so a mismatch would give every evaluated model a train/eval viewpoint
gap rather than measuring the model.

---

## 1. The camera rig — must match exactly

Verified identical across sampled tasks of `Franka-Datasets-v2-5k-LIBERO` and
`Franka-Datasets-v2-30k-LIBERO`. Machine-readable copy: [`camera_rig.json`](camera_rig.json).

| | value |
|---|---|
| embodiment | **franka** (default) |
| camera group | **`camera_fixed`** |
| wrist camera | mounted on the **end-effector body**, hand→camera translation **`[0.05, 0.0, 0.0]`**, quat **`[0, 0.707108, 0.707108, 0]`**, **fovy 75°** |
| third-person camera | `camera_to_world` translation **`[1.0086, 0.0, 1.1904]`** |
| resolution | **640 × 360** |
| fps | **15** |

**The wrist rig is the one that matters most.** The superseded batch had it at
`[-0.074, 0, 0.0292]` with fovy 51.9° — 12.7 cm away, on the opposite side of the hand, and 23°
narrower. If a render comes back at the old rig it is unusable, and the difference is invisible in
a single still frame: it only shows up in how the image moves.

A quick self-check after rendering one episode: compose the inverse end-effector pose with the
recorded `camera_to_world` and confirm the translation is `[0.05, 0, 0]` to 3 decimals, and that it
is constant across frames (it should be rigid — ours measures a per-element std of ~7e-08).

---

## 1b. Appearance must be PINNED — this is new, and it matters more than the rig

The existing demos are **appearance-randomized per demo**: the render pipeline runs
`TextureModder` and `LightingModder` off a per-attempt `variant_seed`, so each of a task's 20
demonstrations has a different table material, a different floor and different lighting. We only
found this by looking at the frames — it is recorded in the render manifest, not in the task's
`domain_randomization` block, which contains position deltas only.

For a 20-demo benchmark that is a problem. The policy has to learn appearance-invariance *and* the
manipulation skill from 20 samples, so the score conflates two abilities and cannot be attributed
to the one under test. Measured on the current demos: a reach task scores 82% while two
manipulation tasks score 2% and 0%, with 99 of 100 rollouts running to the step cap — acting, never
completing.

**So please render all 20 demos of a task under ONE fixed appearance:** one table material, one
floor, one lighting setup, held constant across every demo and every task. Concretely, either
disable `TextureModder` and `LightingModder`, or hold `variant_seed` constant across the batch.

If your pipeline cannot pin them, please say so rather than varying them — we would rather know and
design around it than discover it in the frames again.

## 2. What to render

| file | contents |
|---|---|
| [`tasks.json`](tasks.json) | all 10 tasks, required + spare attempt ids, the rig |
| [`attempts/task_<id>.json`](attempts) | one file per task |
| [`attempts/all_attempts.csv`](attempts/all_attempts.csv) | flat table — `task_id, task_name, operation, role, attempt_id, n_frames, reward_final` |

| task | operation | demos | description |
|---|---|---|---|
| 809 | separate | 20 | separate the sandwich from the juice |
| 811 | reach | 20 | reach target pose |
| 866 | rotate | 20 | rotate the computer mouse |
| 868 | pick | 20 | pick single rigid object |
| 945 | put_into | 20 | put both the potato and the pepper in the bowl |
| 953 | put_spatial | 20 | put the muffin on the left side of the cup |
| 966 | put_on | 20 | put the eyeglasses case on the tissue box |
| 1426 | rearrange | 20 | rearrange three items |
| 1459 | insert | 20 | fit lid onto container |
| 1889 | put_beside | 20 | put the kiwi beside the banana |

**`attempt_id` is the identity — please render exactly those.** They are not arbitrary: they were
selected as the highest-reward demonstrations available for each task, and they are the only ones
with verified trace and goal data. Episode indices are renumbered by every build, so please key
your output on `attempt_id`, not on episode order.

**`role = spare`** lists 40 further attempts per task, ranked. If a required attempt fails to
render, take the highest-ranked spare and say which you substituted — don't silently drop one, as
20 demos is the whole adaptation budget and a missing demo is 5% of it.

---

## 3. Where to upload

Please upload into `uploads/` in this same folder, one directory per task:

```
uploads/
  task_809/
    meta/          LeRobot v3.0 metadata
    data/          parquet
    videos/        observation.images.third_person/ and .wrist/
    render_manifest.json     attempt_id -> episode_index, per episode
  task_811/
  ...
```

`render_manifest.json` is the important one — without an `attempt_id → episode_index` mapping we
have to recover the pairing by matching trajectories, which works but is slower and needs checking.
If your pipeline already emits `task_manifest.json` in the usual shape, that is perfect; please just
make sure `records[]` is populated (two tasks in the previous batch shipped with `records: []` while
having over a thousand episodes).

---

## 4. Anything else

If a task can't be rendered at this rig for a structural reason, please say which and why rather
than substituting a different rig — we'd rather run a 9-task benchmark than one with a mixed rig.

Questions on any of this are welcome before rendering starts; a wrong rig is expensive to discover
afterwards.