Controlling a robot to perform a task is more difficult than commanding a human. A robot needs to be preprogrammed to perform a task. This is achieved by providing the robot with a complete set of step-by-step commands from the beginning till the end. In contrast, to a human, recalling an experience when he was instructed with the same command in a similar situation, a human would be able to guess what intention behind such a command is and could then behave cooperatively. Our objective is to equip the robot with such a capability of recognizing some simple human intentions required of a robot, such as: moving around a corner, moving parallel to the wall, or moving towards an object. The cues used by the robot to make an inference were: the odometer and laser sensor readings, and the human operator’s commands given. Using the Maximum-Likelihood (ML) parameter learning on Dynamic Bayesian Networks, the correlations between these cues and the intentions were modeled and used to infer the human intentions in controlling the robot. From the experiments, the robot was able to learn and infer the above mentioned intentions of the human user with a satisfying success rate.
Use of artificial intelligence based models for learning better policy for maternal and child health
