Human Machine Collaborative Systems (HMCS) have been developed to enhance sensation and suppress extraneous motions or forces during surgical tasks requiring precise motion. However, to date such systems have enforced constraints on the position or path of a tool, but have not considered the dynamics of motion. Also, the focus has been on the effect of guidance of motion during a task, rather than on the learning of motion skills through repetition. We present a pseudo-admittance framework for HMCS design to guide the user's velocity in such tasks. Two different fixture design approaches are analyzed, implemented and compared. Three tests are then conducted, showing the fixtures' promise for both guiding and learning motions with dynamics.