Robots that plan to perform everyday tasks need knowledge of everyday physics. Physics For Robots (PFR) is a representation of part of everyday physics directed towards this need. It includes general concepts and theories, and it has been applied to tasks in cooking. PFR goes beyond most AI planning representation schemes by including natural processes that the robot can control. It also includes a theory of material composition so robots can identify and reason about physical objects that break apart, come together, mix, or go out of existence. Following on Naive Physics (NP), issues about reasoning mechanisms are temporarily postponed, allowing a focus on the characterization of knowledge. However, PFR departs from NP in two ways. (1) PFR characterizes the robot’s capabilities to act and perceive, and (2) PFR replaces the NP goal of developing models of actual common sense knowledge. Instead, PFR includes all and only the knowledge that robots need for planning, which is determined by analyzing proofs showing the effectiveness of robot I/O programs.