潜在計画のためのダイナミクス情報付き行動学習
Dynamics-Informed Action Learning for Goal-Conditioned Latent Planning
潜在世界モデルにおいて、学習時の自己回帰予測への整合と逆ダイナミクスによる行動推定を導入し、テスト時に逆・順ダイナミクスを結合してCEMの初期解を生成することで、視覚制御の計画性能を向上させた研究。
詳しい要約
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著者: Yinuo Jiang, Guangzhi Wang, Yiran Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Cheng Cheng, Ying Shan
分類: cs.CV, cs.RO
原文アブストラクト
Latent world models enable visual control by planning action sequences in compact representation spaces, but a gap remains between how latent dynamics are learned and how they are used for planning. First, teacher-forced training does not expose the dynamics model to the recursively predicted latent states encountered during planning. Second, sampling-based planners typically initialize action search from goal-agnostic proposals, without exploiting the action-conditioned dynamics learned by the world model. We introduce Dynamics-Informed Action Learning (DIAL) to bridge these two gaps. During training, rollout alignment exposes the forward model to its own autoregressive latent predictions, while multi-step inverse supervision trains a lightweight inverse dynamics head to infer actions from latent-state transitions. We further apply the inverse head to model-generated transitions with its parameters held fixed, encouraging predicted rollouts to retain action-relevant information. At test time, DIAL autoregressively couples the inverse and forward dynamics to construct a dynamics-informed action sequence toward the goal, which initializes the mean of the Cross-Entropy Method (CEM) proposal distribution. The subsequent CEM search then refines this initialization using the standard sampling and updating procedure. Experiments across four visual control benchmarks demonstrate that DIAL outperforms existing latent world models and planning baselines in both standard and long-horizon scenarios.