enactic/act-openarm-2-cell-pick_up_cube_mujoco

Model Card for act-openarm-2-cell-pickupcube_mujoco

Action Chunking with Transformers (ACT) policy trained with LeRobot on the k1000dai/openarm-2-cell-pick_up_cube_mujoco-lerobot dataset: a bimanual OpenArm robot performing a cube pick-up task in MuJoCo simulation.

See the full LeRobot documentation at huggingface.co/docs/lerobot.

Model Details

• —Policy type: ACT (Action Chunking with Transformers)
• —Vision backbone: ResNet-18 (ImageNet pretrained)
• —Chunk size / action steps: 100 / 100
• —VAE: enabled (latent dim 32, KL weight 10.0)
• —Transformer: dim_model 512, 8 heads, 4 encoder layers, 1 decoder layer

Inputs

Outputs

Training

• —Dataset: k1000dai/openarm-2-cell-pick_up_cube_mujoco-lerobot
• —Steps: 10,000
• —Batch size: 8
• —Optimizer: AdamW (lr 1e-5, weight decay 1e-4, grad clip 10.0)
• —Normalization: mean/std for visual, state, and action features
• —Seed: 1000

How to Use

from lerobot.policies.act.modeling_act import ACTPolicy

policy = ACTPolicy.from_pretrained("k1000dai/act-openarm-2-cell-pick_up_cube_mujoco")

To evaluate or fine-tune with the LeRobot CLI, pass --policy.path=k1000dai/act-openarm-2-cell-pick_up_cube_mujoco.