| --- |
| license: apache-2.0 |
| tags: |
| - world-model |
| - active-learning |
| - confidence-estimation |
| - robot-learning |
| --- |
| |
| # ConfAL-WM · Model Checkpoints |
|
|
| Model weights for **ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned |
| World Models** (anonymous submission). These artifacts correspond to the |
| "07 · Models & Data" section of the project page. |
|
|
| All checkpoints were produced inside the release codebase; absolute paths and |
| machine-specific metadata have been scrubbed (`<DATA_ROOT>` / `<ANON…>` placeholders). |
|
|
| ## Checkpoints |
|
|
| | File | Card | Description | |
| |---|---|---| |
| | `EVAC warmup v1.ckpt` | EVAC · Warmup v1 | RoboTwin2.0 domain-adapted warmup world model. Starting point for all active-learning rounds (v1 inference + confidence probe scoring). Lightning ckpt, epoch 10 / step 2000. | |
| | `EVAC-v2 weighting none.ckpt` | EVAC-v2 · Weighting None | Selection-only retrained checkpoint (mean-risk acquisition, seed 123). Lightning ckpt, epoch 8 / step 4000. | |
| | `EVAC-v2 weighting frame.ckpt` | EVAC-v2 · Frame | Confidence-guided **frame**-level weighting (mean-risk acquisition, seed 42). Lightning ckpt, epoch 8 / step 4000. | |
| | `EVAC-v2 weighting frame+patch.ckpt` | EVAC-v2 · Frame + Patch | Dense confidence-guided (frame + patch) weighting (mean-risk acquisition, seed 3407). Lightning ckpt, epoch 8 / step 4000. | |
| | `Confidence probe RoboTwin2.0.pt` | Confidence Probe · RoboTwin2.0 | Main C3 confidence probe used in the paper (probe step 6000). Takes EVAC decoder features (h_dec embeddings) and outputs per-frame/patch confidence. | |
| | `Confidence probe AgiBotWorld.pt` | Confidence Probe · AgiBot World | Additional confidence probe trained on AgiBot World (probe step 6000). | |
| | `YOLO RoboTwin2.0.pt` | YOLO · RoboTwin2.0 | Gripper/trajectory-metric detector (left/right gripper) for EWMBench-style evaluation. Ultralytics format; train args sanitized. | |
| |
| ## Usage notes |
| |
| - The `EVAC*` checkpoints are PyTorch-Lightning archives; restore with |
| `LightningModule.load_from_checkpoint(...)` using the model definition in the code release. |
| - The probes are plain `torch.save` state dicts; load with `torch.load(..., map_location="cpu")`. |
| - The YOLO detector can be loaded directly with `ultralytics.YOLO(path)`. |
| - Companion precomputed data (v1 inference outputs, dense confidence maps, baseline |
| scoring artifacts, YOLO annotations, evaluation tables) is available in the dataset repo |
| `anonymous89793/ConfAL-WM-Dataset`. |
|
|
| ## Anonymization |
|
|
| - All absolute filesystem paths inside metadata/pickles were replaced with placeholders |
| (`<DATA_ROOT>/`, `<ANON…>`); no usernames, hostnames, or machine paths remain. |
| - Checkpoint tensors were **not** modified — only metadata strings. |
|
|
