This is the SmolVLA checkpoint reported as HSR-SmolVLA (40k) in the paper Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation, presented at the ICRA 2026 workshop "From Data to Decisions" (1st place, 36 teams).

It is fine-tuned in two phases on top of the public lerobot/smolvla_base. A generalist phase on AIRoA MoMa (Toyota HSR teleoperation episodes) followed by a task-specific top-up on a private subset of the AIRoA ICRA 2026 dataset, distributed only to the competing teams during the workshop. Total parameters, 450 M.

The Toyota HSR has 11 degrees of freedom split across four functionally distinct joint groups (5 arm, 1 gripper, 2 head, 3 holonomic base). The companion paper argues that picking the best fine-tuned checkpoint by the aggregate MSE hides which group is actually responsible for the error, and introduces per-group MSE as a better offline selection signal. This checkpoint is the one that wins the per-group MSE sweep in our setup.

How to load

python

from huggingface_hub import snapshot_download
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy

ckpt_dir = snapshot_download("PauMontagut/per-group-mse-smolvla")
policy = SmolVLAPolicy.from_pretrained(ckpt_dir).eval().cuda()

The policy expects observations in the Toyota HSR layout (two cameras, head and hand, plus an 8-D state vector and a natural-language instruction) and emits action chunks of shape (T, 11) in the order arm_lift, arm_flex, arm_roll, wrist_flex, wrist_roll, gripper, head_pan, head_tilt, base_x, base_y, base_theta.

A WebSocket server skeleton, the I/O contract, the per-group MSE evaluation script and the raw 60-trial robot scores are all in the companion GitHub repository. Robot demo videos, the animated per-group analysis and the full story of the paper are on the project page.

Results

Per-group MSE on a held-out task6911 split, values multiplied by 10³ for readability.

Group	MSE (×10⁻³)
Arm	0.88
Gripper	3.77
Head	0.01
Base	3.18
Total	1.61

Real-robot evaluation on the Toyota HSR, 20 trials over two objects (ceramic mug and Cheez-It box), 4-point ordinal rubric. Mean score 3.50 / 4. The companion paper compares it against the π₀.₅ baseline (80k steps, 4.00 / 4) and our own π₀.₅ fine-tune (3.75 / 4). The π₀.₅ checkpoints are not redistributed because they derive from the private baseline_80k released by the AIRoA workshop under the competition NDA.

Training context

The checkpoint was produced on a single RTX 3090 (24 GB), shared with other lab work, which is why the top-up uses batch 8 in some runs and batch 32 in others. The release is the batch 32, 40k step version (referred to as B32 40k in figures/mse_curves.png of the GitHub repo), which is the best total-MSE checkpoint on the curve.

The release was sanitised with scripts/prepare_hf_release.py to remove lab-local paths from train_config.json. Weights and normalisation statistics were not modified.

What this model is, and what it is not

This is not the model that won the workshop. The competition submission was a π₀.₅ fine-tune that stays behind the AIRoA NDA (see the note in the GitHub repository's README). This SmolVLA checkpoint is one of the intermediate models we tried during the competition rounds, a smaller and cheaper option that we trained and evaluated in parallel with the π₀.₅ track. It is an experimental model that worked, that runs on a single 24 GB GPU, and that ranks the joint groups in the same direction as the larger models, which is the finding the paper is about.

In practice, this means the checkpoint is a useful starting point if you want to extend the fine-tuning on a related dataset, run the per-group MSE analysis on a different robot, or just have a working SmolVLA on the Toyota HSR's 11-DoF layout to build from. It is not intended as a production policy, and it has not been stressed on scenes outside the fine-tuning distribution.

Limitations

Evaluated only on the Toyota HSR with task6911 relocation tasks. Behaviour on a different robot, a different task family or a substantially different scene is not characterised.
Real-robot results are 20 trials per object pair (60 trials total, 20 per model in the paper). The arm-MSE versus total-MSE ranking claim is robust on this sample but the absolute scores will vary with operator, lighting and object pose.
The model has no built-in stopping condition. It always returns an action chunk. Integration code is expected to time the trial and decide when to stop.
Per-group MSE is a better offline signal than total MSE for this setup, but it is still offline. Online behaviour can diverge.

Citation

The arXiv preprint is at arxiv.org/abs/2606.00253.

bibtex

@inproceedings{montagut2026pergroup,
  title  = {Per-Group Error, Not Total {MSE}: Fine-Tuning Vision-Language-Action Models for 11-{DoF} Mobile Manipulation},
  author = {Montagut Bofi, Pau and Garc\'ia Blasco, Mario and Pulli, Tessa and Vincze, Markus},
  booktitle = {ICRA 2026 Workshop on From Data to Decisions},
  year   = {2026},
  eprint = {2606.00253},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url    = {https://arxiv.org/abs/2606.00253}
}

If you also use the SmolVLA backbone, please cite Shukor et al., SmolVLA, a vision-language-action model for affordable and efficient robotics, arXiv:2506.01844.

License

MIT. See the GitHub repository for the full source, the inference server skeleton, and the reproducibility instructions, and the project page for the demos and the interactive summary of the results.