University of Michigan (U-M) researchers have developed a model for a practical technique that robots can use to visually search for or target items in complex environments in a more humanlike manner. The Semantic Linking Maps (SLiM) model teaches robots to seek items in close proximity if they are already in sight of a “landmark object.” SLiM links certain landmark objects in the robot’s memory to other related objects, along with data about the two objects’ typical spatial relationships. The researchers employed SLiM to factor in features of both target and landmark objects, in order to give robots a stronger understanding of how things can be arranged in an environment. Said U-M’s Zhan Zeng, “Being able to efficiently search for objects in an environment is crucial for service robots to autonomously perform tasks. We provide a practical method that enables robot to actively search for target objects in a complex environment.” More->>
