  The ability to reason about and predict the outcome of contacts is paramount
to the successful execution of many robot tasks. Analytical rigid-body contact
models are used extensively in planning and control due to their computational
efficiency and simplicity, yet despite their prevalence, little if any
empirical comparison of these models has been made and it is unclear how well
they approximate contact outcomes. In this paper, we first formulate a system
identification approach for six commonly used contact models in the literature,
and use the proposed method to find parameters for an experimental data-set of
impacts. Next, we compare the models empirically, and establish a task specific
upper bound on the performance of the models and the rigid-body contact model
paradigm. We highlight the limitations of these models, salient failure modes,
and the care that should be taken in parameter selection, which are ultimately
difficult to give a physical interpretation.
