Robotics
LeRobot
English
OpenRAL
rskill
smolvla
vision-language-action
aloha_agilex
vla
robotwin
bimanual
manipulation
Instructions to use OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16 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=OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16 \ --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=OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16 - Notebooks
- Google Colab
- Kaggle
chore: canonical naming migration
Browse files
SKILL.md
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---
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name: smolvla-robotwin
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description: >-
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S1 Vision-Language-Action policy. Capabilities: generalist, pick, place, transfer on block, pot, cup, hammer. SmolVLA (0.45 B, lerobot/smolvla_base) finetuned on the RoboTwin 2.0 unified dataset (50 dual-arm tasks, aloha-agilex embodiment, SAPIEN). Multi-task: action chunks of length 50 across three RGB views (head + per-wrist) driving a 14-DoF dual-arm joint command. Runs on the RoboTwin scene backend through the out-of-process SAPIEN sidecar
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metadata:
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openral_rskill: true # generated discovery view of an rSkill
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schema_version: 0.1
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rskill_id: OpenRAL/rskill-smolvla-robotwin
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manifest: ./rskill.yaml
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role: s1
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kind: vla
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## What it is
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An OpenRAL **Vision-Language-Action policy** (`role: s1`, `kind: vla`). SmolVLA (0.45 B, lerobot/smolvla_base) finetuned on the RoboTwin 2.0 unified dataset (50 dual-arm tasks, aloha-agilex embodiment, SAPIEN). Multi-task: action chunks of length 50 across three RGB views (head + per-wrist) driving a 14-DoF dual-arm joint command. Runs on the RoboTwin scene backend through the out-of-process SAPIEN sidecar
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## Capabilities
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```python
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from openral_rskill import rSkill
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skill = rSkill.from_pretrained("OpenRAL/rskill-smolvla-robotwin")
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# the loader validates embodiment / sensors / runtime / quantization against the target
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# RobotDescription and enforces the weight-license gate before any weights load.
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```
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---
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name: smolvla-robotwin
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description: >-
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S1 Vision-Language-Action policy. Capabilities: generalist, pick, place, transfer on block, pot, cup, hammer. SmolVLA (0.45 B, lerobot/smolvla_base) finetuned on the RoboTwin 2.0 unified dataset (50 dual-arm tasks, aloha-agilex embodiment, SAPIEN). Multi-task: action chunks of length 50 across three RGB views (head + per-wrist) driving a 14-DoF dual-arm joint command. Runs on the RoboTwin scene backend through the out-of-process SAPIEN sidecar. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.
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metadata:
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openral_rskill: true # generated discovery view of an rSkill
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schema_version: 0.1
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rskill_id: OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16
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manifest: ./rskill.yaml
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role: s1
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kind: vla
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## What it is
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An OpenRAL **Vision-Language-Action policy** (`role: s1`, `kind: vla`). SmolVLA (0.45 B, lerobot/smolvla_base) finetuned on the RoboTwin 2.0 unified dataset (50 dual-arm tasks, aloha-agilex embodiment, SAPIEN). Multi-task: action chunks of length 50 across three RGB views (head + per-wrist) driving a 14-DoF dual-arm joint command. Runs on the RoboTwin scene backend through the out-of-process SAPIEN sidecar.
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## Capabilities
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```python
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from openral_rskill import rSkill
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skill = rSkill.from_pretrained("OpenRAL/rskill-smolvla-aloha_agilex-robotwin-bf16")
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# the loader validates embodiment / sensors / runtime / quantization against the target
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# RobotDescription and enforces the weight-license gate before any weights load.
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```
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