Machine-mediated training of Dynamic task completion is typically implemented with passive intervention via virtual fixtures or active assist by means of record and replay strategies. During interaction with a real dynamic system however, the user relies on both visual and haptic feedback real-time in order to elicit desired motions. This work investigates human performance in a Fitts? type targeting task with an underactuated dynamic system. Performance, in terms of number of hits and between-target tap times, is measured while various passive and active control modes are displayed concurrently with the haptic feedback from the simulated system?s own dynamic behavior. It is hypothesized that passive and active assist modes that are implemented during manipulation of simulated underactuated systems could be beneficial in rehabilitation applications. Results indicate that human performance can be improved significantly with the passive and active assist modes.