Motor prosthetic algorithms were recently proposed to combine target and path information to drive reaching arm movements to a static goal. In this paper, we extend two approaches to support goals that may themselves evolve over the duration of the reaching movement. The resulting probabilistic and control-based dynamic-goal reach state equations represent an intermediate level of user flexibility between static-goal reach state equations and unconstrained movement