People have a powerful “physical intelligence” – an ability to infer physical properties of objects and predict future states in complex, dynamic scenes – which they use to interpret their surroundings, plan safe and effective actions, build and understand devices and machines, and communicate efficiently. For instance, you can choose where to place your coffee to prevent it from spilling, arrange books in a stable stack, judge the relative weights of objects after watching them collide, and construct systems of levers and pulleys to manipulate heavy objects. These behaviors suggest that the mind relies on a sophisticated physical reasoning system, and for decades cognitive scientists have been interested in the content of this knowledge, how it is used and how it is acquired. In the last few years, there has been exciting progress in answering these questions in formal computational terms, with the maturation of several different traditions of cognitive modeling that have independently come to take intuitive physics as a central object of study. The goals of this symposium are to: 1) highlight these recent computational developments, focusing chiefly on qualitative reasoning (QR) models and Bayesian perceptual and cognitive models; 2) begin a dialog between leading proponents of these different approaches, discussing a number of dimensions along which the approaches appear to differ and working towards bridging those differences; 3) enrich these models with perspectives from empirical work in cognitive science.