Robots working alongside humans must communicate their intended actions together with the uncertainty that arises from incomplete or ambiguous perception. This paper introduces a mathematical framework for expressing perceptual uncertainty through the motion of a robotic manipulator. Drawing on concepts of approach-avoidance and active perception, robot behavior is organized in a Commitment-Vigilance state space whose dimensions are represented through Laban Effort factors, mapping five uncertainty-related states, namely confidence, curiosity, hesitance, fear and inactivity on the uncertainty continuum. A kinematic analysis decomposes goal-directed end-effector motion into a radial task-progress rate and a tangential target-bearing rate, which realize the two dimensions. From this decomposition, five motion primitives, namely approach, pause, retreat, probe and twitch, are derived and parameterized using eleven kinematic descriptors covering approach and retreat characteristics, pause behavior, gaze angles, end-effector tilt and shiver amplitude. A video-based human-subject study evaluated the recognition of uncertainty-expressive trajectories and the influence of individual descriptors on perceived intensity. For every trajectory the intended behavioral state was the modal response and was selected significantly more often than chance. In single-descriptor comparisons, participants significantly preferred one variant as the more intense expression of the intended state. The results provide a perceptual basis for encoding robot uncertainty in motion and for generating such trajectories autonomously from parametric movement representations. Expressive robot motion videos and questionnaire used in the user study are available at https://anonymous.4open.science/r/aou/.

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