AeroAct: 言語条件付きクワッドローター飛行のためのアクション中心のワールドアクションモデル
AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight
言語指示によるクワッドローター飛行を実現するため、事前学習済みビデオ拡散トランスフォーマーを応用し、視覚・状態・言語から局所的な軌道アクションを予測するアクション中心のワールドアクションモデルを提案した。実機飛行で初めて実証された。
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著者: Xinhong Zhang, Qiyuan Zhu, Yubo Huang, Haolin Chen, Runqing Wang, Yuhao Mo, Zhongxin Chen, Yu Hu, Xinjiang Wang, Jian Sun, Gang Wang
分類: cs.RO
原文アブストラクト
Language-conditioned quadrotor flight requires a policy to ground semantic goals, anticipate the visual consequences of ego-motion, and output control references that remain smooth and dynamically executable under rapidly changing first-person views. Existing aerial vision-language navigation and vision-language-action methods commonly use discrete actions, high-level waypoints, or instantaneous velocity commands, which provide limited supervision about how flight actions change future observations. We present AeroAct, an action-centered world-action model (WAM) for quadrotor navigation. To the best of our knowledge, AeroAct is the first WAM instantiated and demonstrated for real-world aerial flight. The model adapts a pretrained video diffusion Transformer to predict local trajectory-action chunks from egocentric visual history, proprioception, and language. Future first-person frames are used during training as dense consequence supervision, while deployment directly decodes actions without generating future video. To obtain aligned visual, state, language, and dynamically feasible action data, we build a DiffAero-based pipeline with complementary Isaac Lab and 3D Gaussian splatting renderers. We further introduce a low-cost handheld collection device that couples camera observations with motion estimates to recreate flight-like egocentric trajectories, and a self-guidance procedure that improves temporal consistency across overlapping trajectory chunks. Closed-loop simulation and real-world experiments show that temporal visual context improves target tracking and object-search performance, and that WAM-based policies can be executed on a physical quadrotor.
