ConfAL-WM

anonymous89793/ConfAL-WM

ConfAL-WM is a machine learning model from anonymous89793. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

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.

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Updated Aug 18, 2026

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37.5 GB

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Hugging Face

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.ckpt37.3 GB · 99%

At a glance

License: apache-2.0
Access: Public
Created: Aug 17, 2026
Updated: Aug 18, 2026
SHA: 380df9a2

Model card

From the Hugging Face model README

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.

License: apache-2.0
Created: Aug 17, 2026
Updated: Aug 18, 2026