Prior approaches to human guidance using robots inside a building have typically been limited to a single robot guide that navigates a human from start to goal. However, due to their limited mobility, the robot is often unable to keep up with the human's natural speed. In contrast, this paper addresses this difference in mobility between robots and people by presenting an approach that uses multiple robots to guide a human. Our approach uses a compact topological graph representation to formulate the multi-robot guidance problem as a Markov Decision Process (MDP). Using a model of human motion in the presence of guiding robots, we define the transition function for this MDP. We solve the MDP using Value Iteration to obtain an optimal policy for placing robots and evaluate this policy.

It is challenging to build socially-aware robots due to the inherent uncertainty in the dynamics of human behavior. To become socially-aware, robots need to be capable of recognizing activities in their environment to make informed actions in concert with co-present humans. In this paper, we present and validate an event-based method for robots to detect synchronous and asynchronous actions of humans when working as a team in a human-social environment. Our results suggest that our method is capable of detecting synchronous and asynchronous actions, which a step towards building socially aware robots.

Robots that are integrated into day-to-day settings as assistants, collaborators, and companions will engage in dynamic, physically-situated social interactions with their users. Enabling such interactions will require appropriate models and representations for interaction. In this paper, we argue that the dynamic, physically-situated interactions between humans and robots can be characterized as a system of distributed cognition, that this system can be represented using probabilistic graphical models (PGMs), and that the parameters of these models can be learned from human interactions. We illustrate the application of this perspective in our ongoing research on modeling dyadic referential communication.

This paper describes our ongoing research effort to explore how personality types factor into HRI; in particular, the degree of patience a person has when teaching an error-prone robot in a learning from demonstration setting.Our goal is to establish personality metrics that will ultimately allow for the design of algorithms that automatically tune robot behavior to best suit user preferences based on personality.

Developing collaborative robots that can productively operate out of isolation and work safely in uninstrumented, human-populated environments is critically important for advancing the field of robotics. Especially in domains where modern robots are ineffective, we wish to leverage human-robot teaming to improve the efficiency, ability, and safety of human workers. Our work, outlined in this extended abstract, focuses on creating agents capable of human-robot teamwork by leveraging learning from demonstration, hierarchical task networks, multi-agent planning and state estimation, and intention recognition. We briefly describe our recent work within human-robot collaboration, including task comprehension, learning and performing assistive behaviors, and training novice human collaborators to become competent co-workers.

This paper introduces the Affordance Template framework used to supervise task behaviors on the NASA-JSC Valkyrie robot at the 2013 DARPA Robotics Challenge (DRC) Trials. This framework provides graphical interfaces to human supervisors that are adjustable based on the run-time environmental context (e.g., size, location, and shape of objects that the robot must interact with, etc.). Additional improvements, described below, inject degrees of autonomy into instantiations of affordance templates at run-time in order to enable efficient human supervision of the robot for accomplishing tasks.

Socially assistive robotics (SAR) has the potential to combinethe massive replication and standardization of computertechnology with the benefits of learning in a social and tangible(hands-on) context. We are developing HRI methodsfor SAR systems designed to supplement the efforts of humanteachers to personalize education in the classroom. Thisabstract defines and proposes solutions to the computationalchallenges inherent in accomplishing differentiated and personalizededucation utilizing SAR in real-world classrooms. We aim to design robotic systems that are compelling, assistchildren in achieving educational goals, and mitigate developmentalchallenges in a classroom context. To do so, ourapproach must be deeply informed by the needs of our targetusers, children, at all stages of development, and mustadapt to a variety of special needs. In this abstract, we discussmotivation and computational methods for personalizedSAR systems for general, special needs, and mixed multichildeducation contexts. We focus on the personalizationand adaptation of curriculum, feedback, and robot character.

Engagement is a key factor in every social interaction, be it between humans or humans and robots. Many studies were aimed at designing robot behavior in order to sustain human engagement. Infants and children, however, learn how to engage their caregivers to receive more attention.We used a social robot platform, DragonBot, that learned which of its social behaviors retained human engagement. This was achieved by implementing a reinforcement learning algorithm, wherein the reward is the proximity and number of people near the robot. The experiment was run in the World Science Festival in New York, where hundreds of people interacted with the robot. After more than two continuous hours of interaction, the robot learned by itself that making a sad face was the most rewarding expression. Further analysis showed that after a sad face, people's engagement rose for thirty seconds. In other words, the robot learned by itself in two hours that almost no-one leaves a sad DragonBot.

We study the scheduling of human-robot teams where the human and robotic agents share decision-making authority over scheduling decisions. Our goal is to design AI scheduling techniques that account for how people make decisions under different control schema.

For robots to effectively interact with human users, it is necessary that they recognize what people in the environment are doing. This is especially the case when robots are performing complementary tasks since the human users are not following any specific process. There is much uncertainty in how people act and the duration of time they need to perform their actions. In this work, we discuss the use of topic models for such plan and activity recognition tasks. We begin with the development of a domain-independent representation of human postural information obtained from RGB-D sensor data. This representation may be used with Latent Dirichlet Allocation (LDA) topic models as an integration of plan and activity recognition. This is followed by a proposition of extensions to LDA that allow temporal and object relational information to also be used in plan and activity recognition tasks.