  Sufficiently perceiving the environment is a critical factor in robot motion
generation. Although the introduction of deep visual processing models have
contributed in extending this ability, existing methods lack in the ability to
actively modify what to perceive; humans perform internally during visual
cognitive processes. This paper addresses the issue by proposing a novel robot
motion generation model, inspired by a human cognitive structure. The model
incorporates a state-driven active top-down visual attention module, which
acquires attentions that can actively change targets based on task states. We
term such attentions as role-based attentions, since the acquired attention
directed to targets that shared a coherent role throughout the motion. The
model was trained on a robot tool-use task, in which the role-based attentions
perceived the robot grippers and tool as identical end-effectors, during object
picking and object dragging motions respectively. This is analogous to a
biological phenomenon called tool-body assimilation, in which one regards a
handled tool as an extension of one's body. The results suggested an
improvement of flexibility in model's visual perception, which sustained stable
attention and motion even if it was provided with untrained tools or exposed to
experimenter's distractions.
