Learning to understand parameterized commands through a human-robot training task

We propose a method to enable a robot to learn simple, parameterized commands, such as ldquoPlease switch on the TV!rdquo or ldquoCan you bring me a coffee?ldquo for human-robot interaction. The robot learns through natural interaction with a user in a special training task. The goal of the training phase is to allow the user to give commands to a robot in his preferred way instead of learning predefined commands from a handbook. Learning is done in two successive steps. First the robot learns object names. Then it uses the known object names to learn parameterized command patterns and determine the position of parameters in a spoken command. The algorithm uses a combination of hidden Markov models and classical conditioning to handle alternative ways to utter the same command and integrate information from different modalities.