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Sufficiently perceiving the environment is a critical factor in robot motiongeneration. Although the introduction of deep visual processing models havecontributed in extending this ability, existing methods lack in the ability toactively modify what to perceive; humans perform internally during visualcognitive processes. This paper addresses the issue by proposing a novel robotmotion generation model, inspired by a human cognitive structure. The modelincorporates a state-driven active top-down visual attention module, whichacquires attentions that can actively change targets based on task states. Weterm such attentions as role-based attentions, since the acquired attentiondirected to targets that shared a coherent role throughout the motion. Themodel was trained on a robot tool-use task, in which the role-based attentionsperceived the robot grippers and tool as identical end-effectors, during objectpicking and object dragging motions respectively. This is analogous to abiological phenomenon called tool-body assimilation, in which one regards ahandled tool as an extension of one's body. The results suggested animprovement of flexibility in model's visual perception, which sustained stableattention and motion even if it was provided with untrained tools or exposed toexperimenter's distractions.

Deep Active Visual Attention for Real-time Robot Motion Generation: Emergence of Tool-body Assimilation and Adaptive Tool-use