Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes. Yet most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual content. We propose UniAR -- a unified model of human attention and preference behavior across diverse visual content. UniAR leverages a multimodal transformer to predict subjective feedback, such as satisfaction or aesthetic quality, along with the underlying human attention or interaction heatmaps and viewing order. We train UniAR on diverse public datasets spanning natural images, webpages, and graphic designs, and achieve SOTA performance on multiple benchmarks across various image domains and behavior modeling tasks. Potential applications include providing instant feedback on the effectiveness of UIs/visual content, and enabling designers and content-creation models to optimize their creation for human-centric improvements.

This work addresses path planning for reactionless visual servoing of a redundant dual-arm space robot through exploration in the image space. The planner explores the image moment based feature space, impends acceleration to the image features and extends the feature tree. A reactionless visual servoing control law is integrated to extend the tree in the configuration space simultaneously. The proposed algorithm is able to incorporate the necessary coupling between the motions of the the dual arms and the base of the robot to ensure zero base reactions. Additionally, it also gives the flexibility to apply multiple constraints in both the image space and the configuration space. The effectiveness of the proposed framework is exhibited by implementing the algorithm on a numerical model of a 14-DoF dual arm space robot.