Instructions to use fbsh96/so101-sock-ball-act-dualcam-letterbox-100eps-mi300x-b16-20000steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use fbsh96/so101-sock-ball-act-dualcam-letterbox-100eps-mi300x-b16-20000steps with LeRobot:
- Notebooks
- Google Colab
- Kaggle
File size: 1,235 Bytes
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library_name: lerobot
tags:
- lerobot
- act
- so101
- dual-camera
- mi300x
- robotics
---
# SO101 Sock/Ball Pick-Place ACT Dual-Camera Letterbox Checkpoint
This is a LeRobot ACT checkpoint trained on `fbsh96/so101_sock_ball_pick_place_formal_100ep`.
## Training Summary
- Policy: ACT
- Dataset: `fbsh96/so101_sock_ball_pick_place_formal_100ep`
- Episodes: 100
- Frames: 91,600
- Device: AMD Instinct MI300X via ROCm/PyTorch
- Steps: 20,000
- Batch size: 16
- Input features: `observation.state`, `observation.images.hand_cam`, `observation.images.front_cam`
- Action dimension: 12
- Final logged loss: ~0.071
## Camera Preprocessing
The original dataset contains:
- `hand_cam`: 640x480
- `front_cam`: 1280x720
For ACT dual-camera training, both camera tensors must share the same shape. The `front_cam` videos were converted with a non-cropping letterbox transform:
`1280x720 -> scale to 640x360 -> pad to 640x480`
This preserves the full original front-camera field of view and avoids losing edge objects. The trained checkpoint expects both camera inputs as `[3, 480, 640]`.
## Related Baseline
A hand-camera-only baseline is available separately as `fbsh96/so101-sock-ball-act-handcam-100eps-mi300x-b16-20000steps`.
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