| # Mult-skill ACT Models | |
| Multi-skill Action Chunking Transformer models for Piper dual-arm robot. | |
| ## Models | |
| | Model | Description | | |
| |-------|-------------| | |
| | `point_classifier/` | ResNet-18 point classifier (10 classes: point1-point10) | | |
| | `exp1_all_points/` | ACT policy trained on all 150 episodes (10 points combined) | | |
| | `exp2_point1/` ~ `exp2_point10/` | ACT policies trained per-point (15 episodes each) | | |
| | `exp3_act_x/` | Experimental ACT policy | | |
| ## Normalization | |
| `normalization/` contains pre-computed normalization constants for each dataset: | |
| - `all_points_v30_norm.py` - Stats from combined dataset | |
| - `point1_v30_norm.py` ~ `point10_v30_norm.py` - Stats from per-point datasets | |
| ## Usage | |
| ### Download all models | |
| ```bash | |
| pip install huggingface_hub | |
| huggingface-cli download QRP123/mult-skill-act-models --repo-type model --local-dir ./models | |
| ``` | |
| ### Point Classifier Inference | |
| ```python | |
| from point_classifier_model import PointClassifier | |
| import torch | |
| checkpoint = torch.load("point_classifier/point_classifier_best.pth") | |
| model = PointClassifier(num_classes=10, pretrained=False) | |
| model.load_state_dict(checkpoint["model_state_dict"]) | |
| ``` | |
| ## Robot Setup | |
| - Robot: Piper dual-arm (7D + 7D = 14D state/action) | |
| - Cameras: fisheye_left, fisheye_right, wide_mid, wide_top (256x256) | |
| - FPS: 30 | |