Render request — held-out benchmark, 20 tasks × 20 demos
Everything needed is in this folder. Read the attempt lists here, upload the rendered output here.
What: re-render 400 existing demonstrations (20 tasks × 20 attempts) for the default Franka, at the stage1_5k camera rig, with appearance pinned.
Why 20 and not 10 — and why the existing LIBERO-rig renders don't count
Ten of these tasks are currently in the stage1_5k pretraining data, so they cannot be scored against
today's models. They are listed as status: conditional. The other ten are status: active.
It would seem to follow that only the ten active tasks need rendering. It does not, for two reasons:
- The conditional ten become usable the moment a stage-1 run excludes the held-out set — which is planned. Rendering them now avoids a second round trip.
- Their existing renders don't meet the appearance requirement. Tasks 810/815/929/... already
have renders at the correct camera rig in
Franka-Datasets-v2-5k-LIBERO. We checked those manifests: they runTextureModderandLightingModdertoo. So they need re-rendering for appearance regardless of the rig.
So the ask is all 20. If you have to stage it, do the ten marked active first.
Why re-render: these demonstrations currently exist only at a superseded camera rig.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.
| 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 The superseded batch had it at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. Everything in this list held constant across every demo of every task:
| pin | why |
|---|---|
| table material | varies per demo today — six demos of task 953 have six different tables |
| floor / background | varies with it; one demo sits on brown parquet, another on green grass |
| lighting | LightingModder is active per demo |
| object materials / textures | TextureModder recolours the OBJECTS too, so the same mesh reads as a different object between demos |
Concretely: disable TextureModder and LightingModder, or hold variant_seed constant across
the whole batch.
Object meshes do NOT need changing — we verified those are already consistent. The eval scene
loads cup_2, muffin_4, sweet_potato_4, tissue_box_2 from exactly the same
tabletopgen/task_88200032_layout_1_rand_00000 directory the training scene references. The
problem is purely that their materials are randomized, so identical geometry looks like a
different object from demo to demo.
Object PLACEMENT should keep varying — that is the axis the benchmark is meant to test.
If your pipeline cannot pin any of these, 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 |
all 10 tasks, required + spare attempt ids, the rig |
attempts/task_<id>.json |
one file per task |
attempts/all_attempts.csv |
flat table — task_id, task_name, operation, role, attempt_id, n_frames, reward_final |
See tasks.json and attempts/all_attempts.csv
for all 20 tasks, each marked active or conditional.
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.