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.