CF-JEPA: 可制御性の因子分解によるJEPA世界モデルのロバスト性向上
CF-JEPA: Improving Robustness of JEPA World Models via Controllability Factorization
JEPA型世界モデルの潜在空間を制御可能・不可能な部分空間に分離し、背景の妨害情報に頑健なモデルを実現した研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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5. 議論はある？
6. 次に読むべき論文は？
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著者: Morgan Byrd, Robert Wright, Sehoon Ha
分類: cs.RO, cs.LG
原文アブストラクト
Controlling an agent with vision requires being able to separate useful information from irrelevant background information. JEPA-style latent world models seem like a natural approach for this, as they do not perform pixel-level reconstruction; however, they are still sensitive to these distractor signals and experience latent collapse. In this work, we introduce Controllability Factorized JEPA (CF-JEPA), a JEPA-style world model which splits the latent space into controllable and uncontrollable subspaces. This factorization allows us to capture all the distractor information into the uncontrollable region, while we use the control-relevant latent information for our task. With this, we show comparable performance across 2D and 3D control tasks under nominal conditions and improved performance under distracted conditions, where CF-JEPA is the only model that does not experience latent collapse. We also validate our model under distracted conditions for a simulated robot task, highlighting the practical application of such a scheme.
