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act-aic-cable-insertion
farenh/act-aic-cable-insertion
act-aic-cable-insertion is a machine learning model from farenh. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
- Architecture: ACT with ResNet18 backbone, VAE encoder - Dataset: lerobot/alohasiminsertionhumanimage (50 episodes, 25k frames) - Training: 100k steps, batch size 8, chunk size 100, lr 1e-5 - Final loss: 0.006 - Plat…
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Updated Mar 13, 2026
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12.4 GB
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.safetensors620 MB · 100%
At a glance
- Access
- Public
- Created
- Mar 6, 2026
- Updated
- Mar 13, 2026
- SHA
- f674caad
Model card
From the Hugging Face model README
Model
- Architecture: ACT with ResNet18 backbone, VAE encoder
- Dataset:
lerobot/aloha_sim_insertion_human_image(50 episodes, 25k frames) - Training: 100k steps, batch size 8, chunk size 100, lr 1e-5
- Final loss: 0.006
- Platform: Kaggle 2x Tesla T4
Repository Structure
checkpoints/
└── last/
├── pretrained_model/
│ ├── config.json # Model architecture config
│ ├── model.safetensors # Trained weights
│ └── train_config.json # Training hyperparameters
└── training_state.pt # Optimizer state (for resume)
aic_policy_wrapper.py # AIC competition integration wrapper
training_analysis.png # Visualization of predictions vs ground truth
Usage
from lerobot.policies.act.modeling_act import ACTPolicy
model = ACTPolicy.from_pretrained("farenh/act-aic-cable-insertion", subfolder="checkpoints/last/pretrained_model")
model.eval()
AIC Integration
aic_policy_wrapper.py wraps the trained model for the AIC toolkit Docker container. It converts ROS 2 sensor data (camera images + joint states) into ACT input format and outputs robot motion commands.
- Created
- Mar 6, 2026
- Updated
- Mar 13, 2026