ビデオ再計画による暗黙的な状態推定
Implicit State Estimation via Video Replanning
ビデオベースの計画フレームワークにインタラクション時のデータを統合し、モデルパラメータをオンライン更新して失敗した計画を除外することで、暗黙的な状態推定を実現する手法を提案。
詳しい要約
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著者: Po-Chen Ko, Jiayuan Mao, Yu-Hsiang Fu, Hsien-Jeng Yeh, Chu-Rong Chen, Wei-Chiu Ma, Yilun Du, Shao-Hua Sun
分類: cs.RO
原文アブストラクト
Video-based representations have gained prominence in planning and decision-making due to their ability to encode rich spatiotemporal dynamics and geometric relationships. These representations enable flexible and generalizable solutions for complex tasks such as object manipulation and navigation. However, existing video planning frameworks often struggle to adapt to failures at interaction time due to their inability to reason about uncertainties in partially observed environments. To overcome these limitations, we introduce a novel framework that integrates interaction-time data into the planning process. Our approach updates model parameters online and filters out previously failed plans during generation. This enables implicit state estimation, allowing the system to adapt dynamically without explicitly modeling unknown state variables. We evaluate our framework through extensive experiments on a new simulated manipulation benchmark, demonstrating its ability to improve replanning performance and advance the field of video-based decision-making.
