Teleo-Reactive (TR) robotic agent programs comprise sequences of guarded action rules clustered into named parameterised procedures. Their ancestry goes back to the first cognitive robot, Shakey. Like Shakey, a TR programmed robotic agent has a deductive Belief Store comprising constantly changing predicate logic percept facts, and knowledge facts and rules for querying the percepts. In this paper we introduce TR programming using a simple example expressed in the teleo-reactive programming language TeleoR, which is a syntactic extension of QuLog, a typed logic programming language used for the agent’s Belief Store. We give a formal definition of the regression property that rules of TeleoR procedures should satisfy, and an informal operational semantics of the evaluation of a TeleoR procedure call. We then formally express key features of the evaluation in LTL. Finally we show how this LTL formalisation can be used to prove that a procedure’s rules satisfy the regression property by proving it holds for one rule of the example TeleoR program. The proof requires us: to formally link a TeleoR agent’s percept beliefs with sensed configurations of the external environment; to link the agent’s robotic device action intentions with actual robot actions; to specify the eventual physical effects of the robot’s actions on the environment state.