Robot Cooperative Behavior Learning Using Single-Shot Learning From Demonstration and Parallel Hidden Markov Models

For robots to become collaborative assistants, they need to be capable of naturally interacting with users in real environments. They also need to be able to learn new skills from non-expert users. In this letter, we present a novel parallel hidden Markov model (PaHMM) architecture for learning from demonstration (LfD), which allows a robot to learn a sequence of cooperative and non-cooperative behaviors from a single demonstration (single-shot) of a task-based human-robot interaction from two interacting teachers. During teaching, the robot learns both a human-robot interaction model and an object interaction model in order to be able to effectively determine its own behaviors. Experiments with a Baxter robot and several teachers were conducted to validate the ability of the robot to learn both cooperative and non-cooperative behaviors during a task-based interaction. Comparison experiments also show the robustness of our approach to spatial variations from the demonstrated behaviors and tracking errors when compared to other approaches.