pick_red_30k
SmolVLA fine-tuned from lerobot/smolvla_base on
makepluscode/pick_red
for the SO-ARM 101 red-block pick-and-place task. Trained through LeLab.
Companion checkpoint for Part 4 of the book VLA 로보틱스 입문서.
Training
| Base checkpoint | lerobot/smolvla_base |
| Dataset | makepluscode/pick_red — 50 episodes, 27,346 frames |
| Steps | 30,000 |
| Batch size | 8 |
| Optimizer | AdamW, lr 1e-4, cosine decay with warmup |
| Start loss | 0.450 (step 250) |
| Final loss | 0.065 |
| Loss plateau | from ~25,000 steps |
| Hardware | RTX 3060 12GB, CUDA 12.6 |
| Wall time | ~3 h 35 min |
| LeRobot | 0.5.2 (82dffde) |
train_config.json records policy.pretrained_path pointing at the
lerobot/smolvla_base snapshot, and load_vlm_weights: true — the vision and
language weights come from the pretrained base rather than being randomly
initialised.
Trained with LeLab — and what had to be patched
Training ran through LeLab's web UI, not
the LeRobot CLI. Out of the box that does not produce a fine-tune of
smolvla_base, so the vendored LeLab used here carries three changes.
1. Pass a base checkpoint. LeLab builds its training command with
--policy.type only. That is correct for ACT, which has no pretrained base, but
for SmolVLA it means LeRobot constructs the architecture with randomly
initialised vision and language weights and trains only the action expert.
Nothing looks wrong while it happens — the loss falls, checkpoints are written,
inference runs — and LeRobot only warns about it on the PEFT path. The mismatch
shows up solely as policy.pretrained_path: null in train_config.json.
The patch adds a per-policy default and emits --policy.path instead.
2. Use = form for policy arguments. LeRobot's parser reads
--policy.path only as --policy.path=<value> (parse_arg matches on the
--<name>= prefix). Once a policy path is present, get_cli_overrides("policy")
also strips every --policy.* argument, so a space-separated
--policy.device cuda leaves a bare cuda behind and argparse rejects it. All
--policy.* arguments are emitted as --policy.x=y.
3. Resolve the base to a local path, and align camera keys. Two more things break before training starts:
- On Windows, LeRobot turns
policy_pathinto aPath, so the hub idlerobot/smolvla_basebecomeslerobot\smolvla_baseand the hub lookup fails. LeLab now resolves the base withsnapshot_downloadand passes the local snapshot directory. smolvla_basedeclarescamera1/camera2/camera3as its image inputs, while this dataset recordstop/wrist, which fails feature validation. LeLab now reads the expected keys from the baseconfig.json, reads the dataset keys frommeta/info.json, and generates a--rename_map.
For this run that produced:
observation.images.top -> observation.images.camera1
observation.images.wrist -> observation.images.camera2
Selecting SmolVLA in the LeLab training screen is enough; the base
checkpoint and the rename map are filled in by the backend. To train without a
pretrained base, pass an empty policy_path.
Task string
The same sentence is stored in the dataset and must be supplied at inference:
빨간 블록을 집어서 노란색 매트에 놓는다.
Evaluation
Evaluated on the real SO-ARM 101 with the block placed at a single fixed start position — the same condition the demonstrations were collected under. Results are in-distribution and should not be read as evidence of visual or positional generalisation.