| # All-Modality Minimal Model |
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| Script: |
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| ```text |
| scripts/train_all_modalities_model.py |
| ``` |
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| This extends the first minimal model by using every major sample modality in a lightweight way. |
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| ## Modalities Used |
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| Dynamic sensor/action modalities: |
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| - `hand_mocap/left_joints_3d` |
| - `hand_mocap/right_joints_3d` |
| - `full_body_mocap/keypoints` |
| - `full_body_mocap/contacts` |
| - `slam/trans_xyz` |
| - `slam/quat_wxyz` converted by the toolkit into camera rotation matrices |
| - `imu/accel_xyz` |
| - `imu/gyro_xyz` |
| - `depth/depth` |
| - `depth/confidence` |
| - `fisheye_cam0.mp4` |
| - `fisheye_cam1.mp4` |
| - `fisheye_cam2.mp4` |
| - `fisheye_cam3.mp4` |
| - `stereo_left.mp4` |
| - `stereo_right.mp4` |
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| Static/context modalities: |
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| - `slam/point_cloud` |
| - `calibration/*` |
| - caption objects |
| - caption interaction text |
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| By default, the script does **not** include `action_label`, `Sub Task`, or action-description text as input, because those are too close to the prediction target. You can force that with `--include-label-text`, but that should be treated as a leakage/debug run, not a fair action-recognition experiment. |
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| ## Feature Design |
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| The model is still intentionally small: |
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| ```text |
| raw modality -> per-frame or static handcrafted features -> window temporal statistics -> softmax classifier |
| ``` |
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| For each 20-frame window: |
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| - Motion signals use mean/std/min/max/delta/velocity statistics. |
| - Depth uses global depth stats plus a small normalized depth grid and confidence grid. |
| - Each video stream uses color stats, color histograms, a small grayscale grid, and simple edge stats. |
| - Text uses a hashed bag-of-words vector from objects and interaction text. |
| - Point cloud and calibration are included as static episode-level features. |
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| Current feature blocks: |
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| ```text |
| hand_left_joints: 441 |
| hand_right_joints: 441 |
| body_joints: 1092 |
| body_contacts: 147 |
| camera_translation: 21 |
| camera_rotation_matrix: 63 |
| imu_accel_gyro: 42 |
| depth_confidence: 980 |
| video_fisheye_cam0: 686 |
| video_fisheye_cam1: 686 |
| video_fisheye_cam2: 686 |
| video_fisheye_cam3: 686 |
| video_stereo_left: 686 |
| video_stereo_right: 686 |
| caption_objects_interaction_text: 896 |
| slam_point_cloud: 22 |
| calibration: 117 |
| total: 8378 |
| ``` |
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| ## Run Commands |
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| Action prediction: |
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| ```bash |
| cd /path/to/Ropedia |
| source .venv/bin/activate |
| python scripts/train_all_modalities_model.py |
| ``` |
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| Subtask prediction: |
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| ```bash |
| python scripts/train_all_modalities_model.py --target subtask |
| ``` |
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| The first run builds reusable caches in: |
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| ```text |
| outputs/feature_cache/ |
| ``` |
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| ## Current Results |
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| Action-label model: |
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| ```text |
| outputs/min_all_modalities_action_model/ |
| accuracy: 0.9828 |
| balanced_accuracy: 0.9801 |
| macro_f1: 0.9791 |
| weighted_f1: 0.9828 |
| majority_baseline: 0.1375 |
| classes: 18 |
| feature_dim: 8378 |
| test_windows: 291 |
| ``` |
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| Subtask-label model: |
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| ```text |
| outputs/min_all_modalities_subtask_model/ |
| accuracy: 0.9828 |
| balanced_accuracy: 0.9505 |
| macro_f1: 0.9308 |
| weighted_f1: 0.9838 |
| majority_baseline: 0.1448 |
| classes: 14 |
| feature_dim: 8378 |
| test_windows: 290 |
| ``` |
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| ## How To Interpret This |
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| This proves that the full sample can be converted into a complete supervised learning pipeline on this Mac. |
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| It does **not** prove real generalization, because the public sample is one episode and the split is random windows from that same episode. Neighboring windows are correlated. |
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| For a serious embodied-AI experiment: |
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| ```text |
| many episodes |
| -> cache features per episode |
| -> split by episode or task instance |
| -> train on some episodes |
| -> test on unseen episodes |
| ``` |
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| The next useful upgrade is not a bigger classifier. It is a better split and more episodes. |
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