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_path into a Path, so the hub id lerobot/smolvla_base becomes lerobot\smolvla_base and the hub lookup fails. LeLab now resolves the base with snapshot_download and passes the local snapshot directory.
• smolvla_base declares camera1/camera2/camera3 as its image inputs, while this dataset records top/wrist, which fails feature validation. LeLab now reads the expected keys from the base config.json, reads the dataset keys from meta/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.