身体化世界モデルの高次化による計画と制御の実現
Lifting Embodied World Models for Planning and Control
高次元の関節動作空間を直接探索する代わりに、高レベル行動を低レベル関節動作列に変換する軽量ポリシーを訓練し、凍結した世界モデルと組み合わせて高レベル行動から未来観測を予測する「持ち上げられた世界モデル」を提案。人間型エージェントで2Dウェイポイントを高レベル行動とし、低レベル探索より3.8倍精度が向上。
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？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Alex N. Wang, Trevor Darrell, Pavel Izmailov, Yutong Bai, Amir Bar
分類: cs.CV, cs.AI, cs.LG
原文アブストラクト
World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are high-dimensional and difficult to specify: for example, precisely controlling a human agent requires specifying the motion of each joint. This makes the world model hard to control and expensive to plan with as search-based methods like CEM scale poorly with action dimensionality. To address this issue, we train a lightweight policy that maps high-level actions to sequences of low-level joint actions. Composing this policy with the frozen world model produces a lifted world model that predicts a sequence of future observations from a single high-level action. We instantiate this framework for a human-like embodiment, defining the high-level action space as a small set of 2D waypoints annotated on the current observation frame, each specifying a near-term goal position for a leaf joint (pelvis, head, hands). Waypoints are low-dimensional, visually interpretable, and easy to specify manually or to search over. We show that the lifted world model substantially outperforms searching directly in low-level joint space ($3.8\times$ lower mean joint error to the goal pose), while remaining more compute-efficient and generalizing to environments unseen by the policy.
