license: apache-2.0
base_model: lerobot/smolvla_base
tags:
- robotics
- lerobot
- smolvla
- so100
- so101
- vla
- imitation-learning
pipeline_tag: robotics
SmolVLA fine-tuned on SO-100 tic-tac-toe (pick-and-place)
Two full fine-tunes of lerobot/smolvla_base (450M)
on a LeRobot SO-100 "tic-tac-toe" dataset — 180 episodes, 9 pick-and-place tasks
("place the blue cross cube in the <cell> box"), top + wrist cameras (640×480 @30 fps), 6-DOF
joint actions. Seed-fixed split: 144 train / 18 val / 18 test.
Camera mapping used in training: top → observation.images.camera1, wrist → observation.images.camera2.
A note on the third camera
The underlying dataset has only two cameras (top, wrist). The model config, however, lists
three image inputs (camera1, camera2, camera3) — this 3-view layout is inherited from the
lerobot/smolvla_base pretrained model, not from the data:
| config input | source | content |
|---|---|---|
observation.images.camera1 |
dataset top view |
real |
observation.images.camera2 |
dataset wrist view |
real |
observation.images.camera3 |
— | padded empty/black image (no information) |
camera3 is a placeholder kept only to match the base model's expected input shape; SmolVLA tolerates
the missing view by feeding it a blank image. (Note: the saved config.json shows empty_cameras: 0
because the slot comes straight from the base model's input_features rather than being added as an
explicit empty camera — behaviour is unchanged either way.)
For inference you only need to provide the two real views (camera1=top, camera2=wrist),
observation.state, and the task string — the third slot is padded automatically.
Checkpoints
| folder | training data | eval | major-4 joint corr |
|---|---|---|---|
in_distribution/ |
all 9 cells, 20k steps (loss 0.009) | all 9 cells (in-distribution fit) | 0.983 (per-joint all >0.96, MAE ~1.8°) |
heldout_cells/ |
6 cells, 15k steps (best ckpt) | 3 held-out cells (top-left / center / middle-right) | 0.928 (MAE 3.2°) |
The held-out-cell run demonstrates that a pretrained VLA generalizes the pick-place skill across board positions (0.928 on unseen cells vs 0.983 in-distribution) — far above a bespoke Cosmos3 action policy's 0.715 on seen cells. Generalization climbs with training (no overfit collapse).
Usage
Each folder is a standard LeRobot SmolVLA pretrained_model/ (model + pre/post-processors). Load with
LeRobot 0.4.4+:
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("azhicles/smolvla-so100-tictactoe", subfolder="in_distribution")
Provide observations as observation.images.camera1 (top), observation.images.camera2 (wrist),
observation.state (6-DOF), and the task language string; the policy returns a chunked 6-DOF action.
Inference speed
~4.70 s / episode (16 chunked forwards @ 0.284 s) on 1× NVIDIA GB300 — faster and more accurate than the bespoke Cosmos3 action policy baseline.