metadata
license: apache-2.0
library_name: lerobot
base_model: lerobot/smolvla_base
datasets:
- lerobot/svla_so101_pickplace
tags:
- robotics
- lerobot
- smolvla
- vision-language-action
- so-101
- imitation-learning
pipeline_tag: robotics
SmolVLA 路 SO-101 Pick-and-Place (fine-tuned)
Fine-tune of lerobot/smolvla_base (450M VLA)
on the real-robot SO-101 dataset
lerobot/svla_so101_pickplace
(50 teleoperated episodes, 2 cameras up/side, 6-DoF state).
lerobot/svla_so101_pickplace
lerobot/svla_so101_pickplace
lerobot/svla_so101_pickplace
(50 teleoperated episodes, 2 cameras up/side, 6-DoF state).
Training
| Base | lerobot/smolvla_base |
| Dataset | lerobot/svla_so101_pickplace (50 ep / 11,939 frames) |
| Steps | 2,000 (batch size 8) |
| GPU | single T4 (~78 min) |
| Camera mapping | up to camera1, side to camera2 via --rename_map |
| Loss | 0.410 to 0.141 (monotonic) |
Intended use & honest limitations
This is a pipeline-validation / learning run, not a production policy.
- Demonstrates the full real-robot imitation-learning loop: load a real teleoperation dataset, fine-tune a pretrained VLA, converge, ship a checkpoint.
- Only 2,000 steps (~1.3 epochs). The SmolVLA paper uses ~20k steps; expect this checkpoint to under-perform a fully trained one.
- No closed-loop success rate. Evaluation on the physical SO-101 arm (
lerobot-record) was not run (no hardware). Reported signal is training-loss convergence only, which proves the model is learning, not real-world task success.
Load
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("Kaminoikari/smolvla-so101-pickplace-ft")