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.

本文提出了一种启发于人类认知结构的新型机器人运动生成模型，其中包含了一个状态驱动的主动自上而下的视觉关注模块，该模块能够基于任务状态主动改变目标，并在机器人使用工具任务中对机器人的夹持器和工具进行感知，这类似于生物现象 - 工具身体同化。结果表明，该模型的视觉感知具有更好的灵活性。