Practical networked robotic systems rarely implement ideal consensus interactions exactly, since local actuation constraints, sensing biases, and heterogeneous interaction maps may shift the input-output relations used by distributed controllers. Rather than focusing solely on whether consensus occurs, such imperfections can fundamentally alter the structure of the asymptotic collective behavior, typically yielding global or clustered velocity alignment, while exact static agreement arises only in special cases. This paper develops a shifted-interaction framework for analyzing collective alignment under imperfect actuation and sensing. Shifted functions provide a compact representation of local actuator and sensor maps, allowing actuation constraints and sensing biases to be represented by horizontally and vertically shifted monotone interactions. The framework distinguishes nodal shifted actuation from edge-wise shifted sensing. For shifted actuation, a Lyapunov analysis establishes global velocity alignment under compatible actuation ranges, including bounded, non-invertible, and deadzone-type maps. For shifted sensing, a maximum-principle argument and a paired-sensitivity condition establish convergence to velocity clusters over edges with infinite accumulated sensitivity; uniform sensing sensitivity yields exponential global velocity alignment. In the linear case, closed-form expressions characterize asymptotic behavior and identify actuation as a special case of sensing. Simulations illustrate static, globally aligned, and clustered velocity-aligned behaviors.