Learning from human interaction data is a promising approach for developing robot interaction logic, but behaviors learned only from offline data simply represent the most frequent interaction patterns in the training data, without any adaptation for individual differences. We developed a robot that incorporates both data-driven and interactive learning. Our robot first learns high-level dialog and spatial behavior patterns from offline examples of human--human interaction. Then, during live interactions, it chooses among appropriate actions according to its curiosity about the customer's expected behavior, continually updating its predictive model to learn and adapt to each individual. In a user study, we found that participants thought the curious robot was significantly more humanlike with respect to repetitiveness and diversity of behavior, more interesting, and better overall in comparison to a non-curious robot.

Recent studies in human-robot interaction (HRI) have investigated ways to harness the power of the crowd for the purpose of creating robot interaction logic through games and teleoperation interfaces. Sensor networks capable of observing human-human interactions in the real world provide a potentially valuable and scalable source of interaction data that can be used for designing robot behavior. To that end, we present here a fully automated method for reproducing observed real-world social interactions with a robot. The proposed method includes techniques for characterizing the speech and locomotion observed in training interactions, using clustering to identify typical behavior elements and identifying spatial formations using established HRI proxemics models. Behavior logic is learned based on discretized actions captured from the sensor data stream, using a naïve Bayesian classifier. Finally, we propose techniques for reproducing robot speech and locomotion behaviors in a robust way, despite the natural variation of human behaviors and the large amount of sensor noise present in speech recognition. We show our technique in use, training a robot to play the role of a shop clerk in a simple camera shop scenario, and we demonstrate through a comparison experiment that our techniques successfully enabled the generation of socially appropriate speech and locomotion behavior. Notably, the performance of our technique in terms of correct behavior selection was found to be higher than the success rate of speech recognition, indicating its robustness to sensor noise.

Developing interactive behaviors for social robots presents a number of challenges. It is difficult to interpret the meaning of the details of people's behavior, particularly non-verbal behavior like body positioning, but yet a social robot needs to be contingent to such subtle behaviors. It needs to generate utterances and non-verbal behavior with good timing and coordination. The rules for such behavior are often based on implicit knowledge and thus difficult for a designer to describe or program explicitly. We propose to teach such behaviors to a robot with a learning-by-demonstration approach, using recorded human-human interaction data to identify both the behaviors the robot should perform and the social cues it should respond to. In this study, we present a fully unsupervised approach that uses abstraction and clustering to identify behavior elements and joint interaction states, which are used in a variable-length Markov model predictor to generate socially-appropriate behavior commands for a robot. The proposed technique provides encouraging results despite high amounts of sensor noise, especially in speech recognition. We demonstrate our system with a robot in a shopping scenario.