Experience-based representation construction: learning from human and robot teachers

In this paper we address the problem of teaching robots to perform various tasks. We present a behavior-based approach that extends the capabilities of robots, allowing them to learn representations of complex tasks from their own experiences of interacting with a human, and to use the acquired knowledge to teach other robots in turn. A learner robot follows a human or robot teacher and maps its own observations of the environment to its internal behaviors, building at run-time a representation of the experienced task in the form of a behavior network. To enable this, we introduce an architecture that allows the representation and execution of complex and flexible sequences of behaviors and an online algorithm that builds the task representation from observations. We demonstrate our approach in a set of human(teacher)-robot(learner) and robot(teacher)-robot(learner) experiments, in which the robots learn representations for multiple tasks and are able to execute them even in environments with distractor objects that could hinder the learning and the execution process.