Proscriptive Bayesian Programming and Maximum Entropy: a Preliminary Study

Some problems found in robotics systems, as avoiding obstacles, can be better described using proscriptive commands, where only prohibited actions are indicated in contrast to prescriptive situations, which demands that a specific command be specified. An interesting question arises regarding the possibility to learn automatically if proscriptive commands are suitable and which parametric function could be better applied. Lately, a great variety of problems in robotics domain are object of researches using probabilistic methods, including the use of Maximum Entropy in automatic learning for robot control systems. This works presents a preliminary study on automatic learning of proscriptive robot control using maximum entropy and using Bayesian Programming. It is verified whether Maximum entropy and related methods can favour proscriptive commands in an obstacle avoidance task executed by a mobile robot.