Bounding Distributional Shifts in World Modeling through Novelty Detection

• URL: http://arxiv.org/abs/2508.06096v1
• Date: Fri, 08 Aug 2025 07:42:14 GMT
• Title: Bounding Distributional Shifts in World Modeling through Novelty Detection
• Abstract summary: We use a variational autoencoder as a novelty detector to ensure that proposed action trajectories during planning do not cause the learned model to deviate from the training data distribution.<n>The proposed method improves over state-of-the-art solutions in terms of data efficiency.
• Score: 15.354352209595973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work on visual world models shows significant promise in latent state dynamics obtained from pre-trained image backbones. However, most of the current approaches are sensitive to training quality, requiring near-complete coverage of the action and state space during training to prevent divergence during inference. To make a model-based planning algorithm more robust to the quality of the learned world model, we propose in this work to use a variational autoencoder as a novelty detector to ensure that proposed action trajectories during planning do not cause the learned model to deviate from the training data distribution. To evaluate the effectiveness of this approach, a series of experiments in challenging simulated robot environments was carried out, with the proposed method incorporated into a model-predictive control policy loop extending the DINO-WM architecture. The results clearly show that the proposed method improves over state-of-the-art solutions in terms of data efficiency.

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