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weaver-robolab-51task-10k-left
albertalonghini8/weaver-robolab-51task-10k-left
weaver-robolab-51task-10k-left is a machine learning model from albertalonghini8. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Fine-tunes of the released WEAVER action-conditioned multi-view world model on 729 Pi0.5 demos spanning 51 diverse RoboLab tasks (pick-place, bin, plate, stacking, reorientation, tool-use, sorting, food-packing, shelf…
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At a glance
- Access
- Public
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026
- SHA
- b5854074
Model card
From the Hugging Face model README
WEAVER fine-tuned on 51 diverse RoboLab tasks (10k campaign) — model card
Fine-tunes of the released WEAVER action-conditioned multi-view world model on 729 Pi0.5 demos
spanning 51 diverse RoboLab tasks (pick-place, bin, plate, stacking, reorientation, tool-use, sorting,
food-packing, shelf-placement, cleanup — all 10 benchmark tag categories), collected on a fleet of A100s
(plan 009). ~3.5× the count and vastly more task-diverse than the earlier 207-episode
weaver-robolab-pi05-ft.
Two variants differing only in which over-shoulder camera is the exterior_1 input:
| Repo | exterior_1 camera | Use |
|---|---|---|
…-10k-right | over_shoulder right | general world-model prediction |
…-10k-left | over_shoulder left | aligned with the Pi0 policy (its exterior_image_1_left is fed from over_shoulder_left) → the one for policy-in-the-dream / intention work |
Results (held-out val, base WEAVER → fine-tuned, 51-task set)
Right (over_shoulder_right):
| View | FVD ↓ | FID ↓ | LPIPS ↓ |
|---|---|---|---|
| Exterior | 362 → 85 (−77%) | 39.5 → 20.6 | 0.157 → 0.074 |
| Wrist | 696 → 309 (−56%) | 67.9 → 41.2 | 0.418 → 0.247 |
Per-frame (held-out seed): exterior +5.5 dB PSNR / +0.10 SSIM; wrist +5.7 dB / +0.10.
Left (over_shoulder_left — policy-aligned):
| View | FVD ↓ | FID ↓ | LPIPS ↓ |
|---|---|---|---|
| Exterior | → 83.2 | → 22.1 | → 0.076 |
| Wrist | → 289 | → 36.0 | → 0.256 |
Left in-loop val at step 16 000 (base-vs-FT panels + per-frame PSNR/SSIM added by the eval pass). The
left variant essentially matches the right on exterior fidelity (FVD 83 vs 85) and is the one to use for
policy-in-the-dream, since Pi0's exterior_image_1_left is fed from over_shoulder_left.
Training: batch 6, 16 000 steps, single H100, use_compile=False. See plans/009-scale-10k.md,
memory/weaver-finetune.md.
Download & use
huggingface-cli login # + accept the SD3 license (VAE loaded at runtime, not redistributed)
huggingface-cli download <HF_REPO> --local-dir ./weaver-10k
python -m weaver.generate_views --checkpoint ./weaver-10k --output-dir out --split val \
--num-videos 4 --start-idx 0 --overrides dataset.path=<your_dataset> \
dataset.norm_stats_path=<your_dataset>/norm_stats_relabel.json dataset.annotation_dir=annotation_rewards
Fine-tune further with PRETRAINED=./weaver-10k scripts/finetune-weaver-singlegpu.sh. Full pipeline
(collect → encode → train → eval → dream) in NVLabs RoboLab +
this repo's scripts/.
License & attribution
Derived from WEAVER (arnavkj1995 / NVLabs) — respect the upstream license. Checkpoint = fine-tuned FlowWM + reward/critic only; SD3 VAE + CLIP not redistributed (obtain from their gated sources).
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026