2A1-F11 ボトムアップ視覚注視とその連続性評価によるタスク呈示からのキー動作の抽出

概要

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This paper presents a biologically-inspired model employing bottom-up visual attention for robot task learning. Although bottom-up attention enables robots to detect likely important information, discontinuity of the attention as well as its instability causes a challenge in being applied to action learning. The proposed model overcomes the problem by examining spatial and temporal continuity in low-level features for attended locations. Retina filtering and stochastic attention selection, which are integrated with saliency-based visual atteneion, facilitate the process by stabilizing the model's attention while keeping its receptiveness to a new stimulus. An experiment shows that the model can extract key actions from task demonstration.
一般社団法人日本機械学会の論文
2009-05-25