PUMA trained from scratch on the DOMINO 10-task dynamic subset
A PUMA policy trained from scratch (no PUMA-pretrained initialisation) on ten
dynamic manipulation tasks from the DOMINO benchmark. It exists as the external
baseline for a benchmark-validation study of another VLA, so it is published for
reproducibility rather than as a tuned release.
Contents
| Path | What |
|---|
domino10_pumascratch_mlxp_seed0_s30000_20260804/pytorch_model.pt | final weights at step 30000 (9.4 GB) |
Load with upstream PUMA — the file is a plain torch.save of the framework's
state, not a transformers checkpoint, so AutoModel.from_pretrained does not
apply.
Tasks
Ten tasks, the dynamic subset used by the DOMINO paper — five Dynamic
Interception and five Dynamic Tracking:
grab_roller, handover_block, place_a2b_left, place_can_basket,
shake_bottle, beat_block_hammer, move_pillbottle_pad,
place_bread_basket, place_container_plate, press_stapler
50 demonstrations per task, all used for training. There is no validation split
and no checkpoint selection: the released checkpoint is the last one, at exactly
max_steps.
Training
| |
|---|
| Initialisation | from scratch, on the Qwen3-VL-4B-Instruct-Action base VLM |
| Steps | 30000 |
| Global batch | 64 (16 per GPU x 4 GPUs, no gradient accumulation) |
| Hardware | 4x NVIDIA H200 |
| Wall-clock | 42.6 h |
| Attention | flash_attention_2 |
| Data mixture | the ten tasks above at equal weight |
| Final training loss | 0.0012 |
| Runtime | torch 2.6.0+cu124, flash-attn 2.7.4.post1 |
What the model actually sees
Worth stating explicitly, because it is easy to assume otherwise when comparing
against another policy trained on the same files:
- Cameras: three —
cam_high, cam_left_wrist, cam_right_wrist.
- Proprioceptive state: not part of the batch. The state modality is
declared and its statistics are computed, but upstream's
include_state: false
keeps it out of the sample, so the policy is vision-and-language only.
- Actions: 14-dim absolute joint targets, chunk of 16.
- Temporal context: four history frames at stride 4 (a 12-step window),
supplied as 64x64 optical flow rather than as full-resolution frames.
- World-model supervision: four future frames at stride 4.
- Images: cropped at scale 0.95 then resized to 224x224.
Data conversion
Converted from the benchmark's HDF5 episodes to LeRobot v2.1 with PUMA's own
robotwin2lerobot scripts, unmodified. Two properties of that converter are
worth knowing because they are inherited by anything trained on its output:
- frames are decoded with OpenCV and stored without a channel swap, so the video
holds BGR in RGB slots — training and evaluation must agree on this, and
upstream's evaluation path does not apply the swap;
fps is recorded as 15 in the metadata while the source data is 30 Hz, and the
last frame of every episode is dropped.
Differences from upstream defaults
attn_implementation: sdpa -> flash_attention_2.- A
domino_paper10 entry added to the dataset mixture table for the ten tasks.
Everything else, including the instruction sampling and the evaluation interface,
is upstream's.
Reproducibility notes
- The
seed0 in the folder name is this study's run-naming convention. PUMA's own
RNG seed for this run was upstream's default, 42 — it was not threaded from
the study's seed. One training run, one seed: nothing here supports a claim
about seed-to-seed variance.
- These weights have not been evaluated in this repository. The numbers
reported in the study for a PUMA baseline came from PUMA's published
checkpoint, not from this from-scratch run.
Licence
Derived from upstream PUMA and from the Qwen3-VL-4B-Instruct-Action base model;
use is subject to their terms. Consult those projects before redistribution.
