Achieving human-like musical expressivity in musical robots has been a long standing goal for roboticists and musicians who work in the emerging field of robotic musicianship. In robotic drumming, a major subfield of robotic musicianship, research towards achieving this goal focuses on the development of musical analysis and improvisational algorithms, design of robots that respond in a contextually appropriate way to music and methods to utilize gestures in collaborative music making. These research address the "what" in robotic drumming, that is, what are the notes, dynamics, inter-onset times and the rhythmic parameters that a robot would play in response to the musical analysis. Much less research has gone into "how" each of these notes are played and how to enrich the acoustical and timbral expression of robotic drummers. The main technical contribution of this work is a generative model for stroke generation in robotic drummers based on the physics of the interaction between human hand and a drumstick. A wide palette of strokes such as multiple bounce strokes, double and triple strokes can be generated using the same model by varying one single parameter, the time-varying torque applied by the thumb on the stick. A number of post processing tools have been developed to further modify the strokes produced by the physical model in an effort to simulate and capture the acoustic variety and richness of natural human drumming. We also present results from a pilot study in which 22 subjects participated in a listening test in which they listened to 10 pairs of audio files, each consisting of a human and a robotic drummer playing multiple bounce strokes. Inferential confidence interval approach was used to determine the maximum probable difference between the data obtained from the listening test and a simulated experiment in which the robotic drummer was perceptually indistinguishable from a human drummer. The results indicates that this generative physics model marks an important step towards achieving human-like musical expressivity in robotic drummers.