While most research in Human-Robot Interaction (HRI) studies one-off or short-term interactions in constrained laboratory settings, a growing body of research focuses on breaking through these boundaries and studying long-term interactions that arise through deployments of robots “in the wild”. Under these conditions, robots need to incrementally learn new concepts or abilities (i.e., “lifelong learning”) to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The second edition of the “Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)” workshop aims to address the developments and challenges in these areas and create a medium for researchers to share their work in progress, present preliminary results, learn from the experience of invited researchers and discuss relevant topics. The workshop focuses on studies on lifelong learning and adaptivity to users, context, environment, and tasks in long-term interactions in a variety of fields such as education, rehabilitation, elderly care, collaborative tasks, service, and companion robots.

Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child’s engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4–6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups.

In recent years, researchers have developed technology to analyze human facial expressions and other affective data at very high time resolution. This technology is enabling researchers to develop and study interactive robots that are increasingly sensitive to their human interaction partners' affective states. However, typical interaction planning models and algorithms operate on timescales that are frequently orders of magnitude larger than the timescales at which real-time affect data is sensed. To bridge this gap between the scales of sensor data collection and interaction modeling, affective data must be aggregated and interpreted over longer timescales. In this paper we clarify and formalize the computational task of affect interpretation in the context of an interactive educational game played by a human and a robot, during which facial expression data is sensed, interpreted, and used to predict the interaction partner's gameplay behavior. We compare different techniques for affect interpretation, used to generate sets of affective labels for an interactive modeling and inference task, and evaluate how the labels generated by each interpretation technique impact model training and inference. We show that incorporating a simple method of personalization into the affect interpretation process - dynamically calculating and applying a personalized threshold for determining affect feature labels over time - leads to a significant improvement in the quality of inference, comparable to performance gains from other data pre-processing steps such as smoothing data via median filter. We discuss the implications of these findings for future development of affect-aware interactive robots and propose guidelines for the use of affect interpretation methods in interactive scenarios.

For practical reasons, most human-robot interaction (HRI) studies focus on short-term interactions between humans and robots. However, such studies do not capture the difficulty of sustaining engagement and interaction quality across long-term interactions. Many real-world robot applications will require repeated interactions and relationship-building over the long term, and personalization and adaptation to users will be necessary to maintain user engagement and to build rapport and trust between the user and the robot. This full-day workshop brings together perspectives from a variety of research areas, including companion robots, elderly care, and educational robots, in order to provide a forum for sharing and discussing innovations, experiences, works-in-progress, and best practices which address the challenges of personalization in long-term HRI.

In this paper we present additional results from a prior study of speech-based games to promote early literacy skills through child-robot interaction [6]. The additional data and results support our original conclusion, that pronunciation analysis software can be an effective enabler of speech child-robot interactions. We also include a comparison of other pronunciation services, an updated version of the SpeechAce API and a new technology from Soapbox Labs. We reflect on some lessons learned and introduce a redesigned version of the game interaction called `RhymeRacer' based on the results and observations from both data collections.

Though substantial research has been dedicated towards using technology to improve education, no current methods are as effective as one-on-one tutoring. A critical, though relatively understudied, aspect of effective tutoring is modulating the student's affective state throughout the tutoring session in order to maximize long-term learning gains. We developed an integrated experimental paradigm in which children play a second-language learning game on a tablet, in collaboration with a fully autonomous social robotic learning companion. As part of the system, we measured children's valence and engagement via an automatic facial expression analysis system. These signals were combined into a reward signal that fed into the robot's affective reinforcement learning algorithm. Over several sessions, the robot played the game and personalized its motivational strategies (using verbal and non-verbal actions) to each student. We evaluated this system with 34 children in preschool classrooms for a duration of two months. We saw that (1) children learned new words from the repeated tutoring sessions, (2) the affective policy personalized to students over the duration of the study, and (3) students who interacted with a robot that personalized its affective feedback strategy showed a significant increase in valence, as compared to students who interacted with a non-personalizing robot. This integrated system of tablet-based educational content, affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring.

We deployed an autonomous social robotic learning companion in three preschool classrooms at an American public school for two months. Before and after this deployment, we asked the teachers and teaching assistants who worked in the classrooms about their views on the use of social robots in preschool education. We found that teachers' expectations about the experience of having a robot in their classrooms often did not match up with their actual experience. These teachers generally expected the robot to be disruptive, but found that it was not, and furthermore, had numerous positive ideas about the robot's potential as a new educational tool for their classrooms. Based on these interviews, we provide a summary of lessons we learned about running child-robot interaction studies in preschools. We share some advice for future researchers who may wish to engage teachers and schools in the course of their own human-robot interaction work. Understanding the teachers, the classroom environment, and the constraints involved is especially important for microgenetic and longitudinal studies, which require more of the school's time-as well as more of the researchers' time-and is a greater opportunity investment for everyone involved.

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children.

In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot's actions to an individual's strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants' puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot's personalizations were less sophisticated than what a human tutor can do, we still witnessed a "one-sigma" improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions.