smolVLA-IsaacLab-picknplace-50epoch

Cache-SCA/smolVLA-IsaacLab-picknplace-50epoch

smolVLA-IsaacLab-picknplace-50epoch is a robotics model from Cache-SCA. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for lerobot. The card lists the license as apache-2.0.

lerobot/smolvlabase 를 IsaacLab 시뮬레이션 SO101 pick & place 단일 task 데이터셋 CoRL2026-CSI/IsaacLab-SO101pickplacebaseCaP100epi10fps 으로 50 epoch 파인튜닝한 SmolVLA 정책.

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.safetensors907 MB · 100%

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BF16447M · 99%

Base models

lerobot/smolvla_base

Datasets

CoRL2026-CSI/IsaacLab-SO101_pick_place_baseCaP_100epi_10fps

Model card

From the Hugging Face model README

smolVLA · IsaacLab SO101 Pick & Place (single-task, 50 epoch)

lerobot/smolvla_base 를 IsaacLab 시뮬레이션 SO101 pick & place 단일 task 데이터셋 CoRL2026-CSI/IsaacLab-SO101_pick_place_baseCaP_100epi_10fps 으로 50 epoch 파인튜닝한 SmolVLA 정책.

이 체크포인트는 full model (model.safetensors) 입니다 — LoRA adapter 가 아니며, 그대로 로드해 사용합니다.

Model details

Base model: lerobot/smolvla_base (SmolVLM2-500M-Video-Instruct VLM + action expert)
Robot: SO101 (6-DOF, gripper 포함) — IsaacLab 시뮬레이션
Cameras: top, left_wrist (480×640) — 정책 키 camera1(left_wrist) / camera2(top) 로 rename
Inputs: observation.state[6] + 카메라 2개 + language instruction (task)
Output: action[6] (joint position)
Action chunking: chunk_size=50, n_action_steps=50

학습 방식

VLM frozen + action expert only — SmolVLA 공식 표준 학습 방식 (SmolVLA paper, arXiv:2506.01844).

구성요소	상태
VLM backbone (SmolVLM2)	❄️ 완전 Frozen (`freeze_vision_encoder=true`)
Action expert	🔥 학습 (`train_expert_only=true`)
PEFT / LoRA	사용 안 함

Training hyperparameters

항목	값
Dataset	IsaacLab-SO101_pick_place_baseCaP_100epi_10fps — 100 episodes / 34,264 frames / 10 fps
Epochs / Steps	50 epoch / 6,700 steps
Global batch size	256 (micro batch 128 × 2 GPU)
Optimizer	AdamW — lr `1e-4`, weight_decay `1e-10`, grad_clip_norm `10.0`
LR scheduler	cosine_decay_with_warmup — warmup 1,000 / decay 30,000 / peak_lr `1e-4` / decay_lr `2.5e-6`
chunk_size / n_action_steps	50 / 50
Seed	1000
Dataloader workers	16
Mixed precision	no (bf16 inference)
Image augmentation	ColorJitter (brightness/contrast/saturation/hue) + SharpnessJitter — 기하학적 변형(회전/이동/반전) 없음 (VLA 좌우 의미 보존)
Hardware	2 × NVIDIA H100 80GB
Final loss	0.013

Camera rename

LeRobot dataset 의 카메라 키와 SmolVLA 정책 키 매핑:

Dataset key	Policy key
`observation.images.left_wrist`	`observation.images.camera1`
`observation.images.top`	`observation.images.camera2`

Input / Output 규정

Input: observation.state[6] (joint position) + 카메라 2개 + language instruction(task) 만
Output: action[6] (joint position) 만
데이터셋의 ee_pos / gripper_binary / state.radian_urdf0 / action.radian_urdf0 는 학습에서 제외
SmolVLA 정책은 카메라 슬롯이 3개(camera1/2/3)로 고정이라 camera3 슬롯이 config 에 존재하지만, 데이터셋 카메라는 2개뿐이라 실제로 데이터가 흐르는 카메라는 2개입니다.

Usage

from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy

policy = SmolVLAPolicy.from_pretrained("CoRL2026-CSI/smolVLA-IsaacLab-picknplace-50epoch")

Citation / Acknowledgement

Built on top of LeRobot and the SmolVLA base checkpoint. Project: CoRL 2026 CSI submission.

Framework versions

LeRobot 0.5.2

Task: Robotics
Library: lerobot
License: apache-2.0
Created: May 16, 2026
Updated: May 16, 2026