Open-Weight Models

Self-contained FULL weights (merged LoRA r16/a32 onto step917, verified to bf16 precision). No peft/adapter path needed. For LIBERO eval: --policy.path with rename camera1/camera2.

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.21.0

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

lerobot/smolvlabase를 SO-101 단일팔 픽앤플레이스 데이터셋 Masterji/dataset으로 파인튜닝한 SmolVLA 정책입니다. 이 저장소는 최종 체크포인트(step 10000)입니다. - 태스크 문장: Pick up the eclipse and place it on the box - 데이터: 30 에피소드 중 0~26번(27개)으로 학습, 27~29번(3개)은 held-out 평가용 - 관측: observation.state(6), 카메라 top·wrist(640×480) → 정책 입력 camera1·camera2로 매핑 - 액션: 6관절 위치(shoulderpan, shoulderlift, elbowflex, wristflex, wristroll, gripper) flow matching 손실이라 노이즈·시간 표본에 따라 값이 흔들립니다. 다른 모델의 손실과 직접 비교할 수 없습니다. eval loss가 가장 낮은 체크포인트와 최종 체크포인트를 같은 held-out 표본·같은 시드로 비교했습니다. 값은 액션 청크의 평균 절대 오차(관절 도, 그리퍼는 0~100 범위)입니다. flow matching의 eval loss와 실제로 샘플링한 액션의 오차는 순위가 다를 수 있어 둘을 함께 적습니다. 관절별 50스텝 평균 오차: shoulderpan.pos 4.39, shoulderlift.pos 8.90…

lerobot/smolvlabase를 SO-101 단일팔 픽앤플레이스 데이터셋 Masterji/dataset으로 파인튜닝한 SmolVLA 정책입니다. 이 저장소는 eval loss 최저 체크포인트(step 3500)입니다. - 태스크 문장: Pick up the eclipse and place it on the box - 데이터: 30 에피소드 중 0~26번(27개)으로 학습, 27~29번(3개)은 held-out 평가용 - 관측: observation.state(6), 카메라 top·wrist(640×480) → 정책 입력 camera1·camera2로 매핑 - 액션: 6관절 위치(shoulderpan, shoulderlift, elbowflex, wristflex, wristroll, gripper) flow matching 손실이라 노이즈·시간 표본에 따라 값이 흔들립니다. 다른 모델의 손실과 직접 비교할 수 없습니다. eval loss가 가장 낮은 체크포인트와 최종 체크포인트를 같은 held-out 표본·같은 시드로 비교했습니다. 값은 액션 청크의 평균 절대 오차(관절 도, 그리퍼는 0~100 범위)입니다. flow matching의 eval loss와 실제로 샘플링한 액션의 오차는 순위가 다를 수 있어 둘을 함께 적습니다. 관절별 50스텝 평균 오차: shoulderpan.pos 4.70, shoulderlift.pos…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…