Instructions to use adityaGautham/act_rgb_plastic_20260811 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use adityaGautham/act_rgb_plastic_20260811 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
ACT โ SO-101: pick up the plastic box, place it on the teal cap
ACT policy (default lerobot config, 52M params, ResNet18 backbone) trained with lerobot v0.6.1 on 32 teleop episodes (11,179 frames @ 30 fps) recorded 2026-08-11 on an SO-101 follower.
- Cameras (must match at inference):
top960ร600,wrist640ร480, RGB @ 30 fps - State/action: 6-dim joint positions (degrees)
- Data: merge of the six
adityaGautham/rgb_plastic_20260811_*datasets; normalization stats recomputed over the merged set - Training: batch 8, 100k steps, ~2 h on RTX 5090; final loss 0.052 (L1 0.052)
Run
lelab: use adityaGautham/act_rgb_plastic_20260811@root as the model reference, or CLI:
lerobot-rollout \
--strategy.type=base \
--policy.path=adityaGautham/act_rgb_plastic_20260811 \
--robot.type=so101_follower \
--robot.port=<your port> --robot.id=<your id> \
--robot.cameras="{ top: {type: opencv, index_or_path: <i>, width: 960, height: 600, fps: 30}, wrist: {type: opencv, index_or_path: <j>, width: 640, height: 480, fps: 30}}" \
--task="pick up the plastic box and place it on the teal cap" --duration=30
Requires lerobot โฅ 0.6.0 (external normalization processors). Trained on 32 episodes from a single scene โ expect sensitivity to camera pose, object appearance, and lighting.
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