Autonomous development of natural interactive behavior for robots and embodied agents

The goal of the research described in this dissertation is to design and evaluate a novel control architecture for autonomous interactive robots and agents that allows the robot/agent to learn natural interaction protocols (both implicit and explicit) using unsupervised machine learning and data mining techniques. A natural interaction protocol can be roughly defined as a kind of multi-scale synchrony between interaction partners; that resembles human interaction mechanisms and allows untrained humans to interact seamlessly with robots and agents that employ it. Our main application domain in this dissertation is learning and executing humanlike nonverbal interaction protocols. Learning is achieved in three stages: 1. In the first stage (Interaction Bubbling), the robot tries to discover the basic actions related to the interaction by watching human-human data collected in a specific kind of interactive situations (e.g. an expert trying to explain an assembly task to a novice human). The algorithms used in this stage are based on unsupervised discovery of change points and recurring motifs in time series. 2. During the second stage (Interaction Structure Learning), the robot builds a hierarchical dynamical/probabilistic system describing the interaction protocol used in this kind of interactions at several time scales and levels of abstraction. (e.g. body alignment, gaze control, proximities, etc). The resulting model is completely grounded in robot’s experience as all the algorithms used to generate it are unsupervised and sensor-driven. 3. The third stage of the robot development (Adaptation through Interaction) involves adjustment of the parameters of the hierarchical dynamical system learned in the second stage during actual interactions with human partners in the same kind of interactions. The first two stages of the development are offline and require no interaction between the robot/agent and human partners and they are referred to as the Learning by Watching phases. The third phase requires real world interaction between the robot and human partners. The combined operation of learning performed by the complete system is called Autonomous Development of Natural Interactive Behavior (ADNIB). After learning, a simulation-theory inspired algorithm is used to drive the robot in real interactions. This algorithm combines both bottom-up and top-down activation approaches. To support this grounded learning and control system the proposed architecture uses only unsupervised learning techniques in all its components. This architecture is called the Embodied Interactive Control Architecture (EICA) and is designed as a set of abstraction levels called Levels of Specification. The lowest level of specification is called L0EICA and it was designed as a generic reactive robotic architecture to support a common action integration and synchronization mechanism between different processes in the robot. The level of specification responsible of implementing ADNIB is called LiEICA. The system was intensively evaluated in four steps. Firstly, every developed algorithm or subsystem was evaluated using both synthetic and real world data. Secondly, L0EICA was used to develop a base reactive gaze controller and evaluated using both simulation and real world experiments and showed human-like behavior. Thirdly, every stage of ADNIB as implemented in LiEICA was evaluated separately using simulations and real world studies. Finally, the learned system was evaluated in two different scenarios (guided navigation and explanation situations) and compared to the simpler L0EICA controller in terms of naturalness of robot’s final behavior.