Instructions to use FelishLeon/smolvla_rm65_stack_47ep_s320000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use FelishLeon/smolvla_rm65_stack_47ep_s320000 with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=FelishLeon/smolvla_rm65_stack_47ep_s320000 \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=FelishLeon/smolvla_rm65_stack_47ep_s320000 - Notebooks
- Google Colab
- Kaggle
SmolVLA RM65 叠圆柱块(47 条,320000 步,夹爪滞回)
从 smolvla_base 微调,第 320000 步检查点。这套权重代替原来的 30 条、1000 步模型。训练 47 条,留出 3、35、37、41、49。等效 batch 64(小批次 4,梯度累积 16),320000 个小批次等于 20000 次优化器更新。视觉编码器未冻结。
状态 7 维,相机 camera1(front)和 camera2(astra),关节动作为相对当前状态的差分,夹爪为绝对值。
夹爪输出使用滞回:连续值降到 0.35 及以下输出 0(合上),升到 0.65 及以上输出 1(张开),中间保持上一状态。第一帧按 0.5 判定。
任务语言用数据集原句:
Stack three cylinder blocks. Grasp the left cylinder.Stack three cylinder blocks. Place it on the middle cylinder and release.Stack three cylinder blocks. Grasp the right cylinder.Stack three cylinder blocks. Place it on the stack and release.
边缘端加载这套权重前,用下面两个文件覆盖本机 lerobot 里的同名文件,否则后处理里的 gripper_hysteresis_processor 无法注册:
edge_patch/configuration_smolvla.py→lerobot/policies/smolvla/configuration_smolvla.pyedge_patch/processor_smolvla.py→lerobot/policies/smolvla/processor_smolvla.py
推理时 n_action_steps=16,empty_cameras=0。关节指令为 q_cmd[:6] = q_now[:6] + pred[:6],夹爪使用滞回后的绝对值。
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