  Haptic feedback can improve safety of teleoperated robots when situational
awareness is limited or operators are inattentive. Standard potential field
approaches increase haptic resistance as an obstacle is approached, which is
desirable when the operator is unaware of the obstacle but undesirable when the
movement is intentional, such as when the operator wishes to inspect or
manipulate an object. This paper presents a novel haptic teleoperation
framework that estimates the operator's attentiveness to obstacles and dampens
haptic feedback for intentional movement. A biologically-inspired attention
model is developed based on computational working memory theories to integrate
visual saliency estimation with spatial mapping. The attentiveness map is
generated in real-time, and our system renders lower haptic forces for
obstacles that the operator is estimated to be aware of. Experimental results
in simulation show that the proposed framework outperforms haptic teleoperation
without attentiveness estimation in terms of task performance, robot safety,
and user experience.
