ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models
Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often concentrated in localized spatiotemporal regions such as robot arms, manipulated objects, contact areas, and occluded objects. This paper presents ConfAL-WM, a confidence-guided active learning framework for post-training embodied world models. Building upon EnerVerse-AC (EVAC), we attach a lightweight confidence probe to UNet decoder features and predict dense confidence maps in the latent space. These maps are aggregated into task-, frame-, and patch-level scores, enabling data-budget alloc...
| Authors | Xiang Liu, Kunwei Wu, Miao Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 August 2026 |
| Last updated at source | 2 October 2026 |
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