Every Embodied SmolVLA MuJoCo Pick-and-Place
This is the Datawhale staging repository for the SmolVLA checkpoint used in the Every Embodied AMD ROCm policy reproduction course.
Current status
The protected rebuilt checkpoint in weights/ is the weighted_000500
checkpoint. It passed the strict closed-loop protocol with:
- red-mug instruction:
27/30 strict physical successes;
- blue-mug instruction:
30/30 strict physical successes;
- overall:
57/60 (95.00%).
This is a fine-tuned SmolVLA policy, not a zero-shot foundation-model result.
The matching dataset manifest, normalization/preprocessing configuration and
evaluation summaries are intentionally documented separately from the model
file. Raw training data and optimizer state are not included.
Task and interface
- Simulator: MuJoCo
SimpleEnv2
- Robot: OMY-style arm
- Observations: two RGB cameras, 6-D robot state, and a language instruction
- Action: 7-D joint/gripper command
- Control frequency: 20 Hz
- Instructions:
Place the red mug on the plate.Place the blue mug on the plate.
Strict evaluation
The preserved evaluation protocol uses forced red/blue instructions, seeds 0-29, and at most 600 action steps. Physical success requires the legacy task predicate plus a real lift of at least 0.03 m for at least three control ticks and a final upright cosine of at least 0.7.
The protected model file is:
weights/model.safetensors- SHA256:
abc335dbd4d4fdbfca8b188b22588b3d768747c9b4ee3051593a4a506c893e0d
The complete file list and checksums are in
SHA256SUMS.txt.
Loading the protected checkpoint
Download this repository with huggingface_hub or the Hugging Face web UI,
then point the Every Embodied SmolVLA Notebook at the local weights/
directory. The model must be evaluated with the matching camera order,
normalization files, action bridge and strict physical-success predicate.
It should not be evaluated with the ACT/Pi0 action interface.
Artifact scope
This repository contains the protected model artifact, its configs, checksums
and strict evaluation evidence. The raw dataset and optimizer state are
omitted; use the course's documented data preparation and training Notebook
to reproduce the recipe.
