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Manuela Uliano

Publications and source records attributed to Manuela Uliano.

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The functional and temporal roles of gaze evolve across the phases and constraints of multi-stage robot-mediated manipulation

Goal-directed eye movements are a fundamental component of visuomotor control, enabling humans to anticipate and guide their actions. For this reason, they are increasingly used in human-robot interaction to estimate users' goals. However, during manipulation, fixations may reflect either an intended future action or the need to visually monitor the robotic proxy due to altered embodiment. How predictive and monitoring-related gaze are organized across the different phases of a constrained robot-mediated manipulation remains unclear. Here we address this question by investigating gaze behavior during goal-directed telemanipulation to characterize how visuomotor control adapts to altered embodiment in a multi-stage task. Our findings show that gaze remains strongly aligned with task goals, preserving its predictive role even during robot-mediated manipulation. At the same time, gaze frequently alternates between the robotic end-effector and the manipulated object, revealing increasing online monitoring. The presence and geometry of obstacles modulate the timing and distribution of these fixations, delaying attention to the final target when intermediate constraints become more demanding. These findings show that predictive gaze is not lost under altered embodiment but reorganized in response to changes in sensory feedback and control demands. More broadly, they highlight the flexibility of the human visuomotor system when the natural sensorimotor coupling is disrupted and suggest that gaze should be interpreted contextually rather than treating every fixation as direct evidence of user intention in human-robot interaction.

cs.RO

A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates are aligned.

cs.RO