Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,45 @@
+---
+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 (persistent-risk acquisition, seed 42). 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.