This work presents a general-purpose character reasoning model intended for usage by autonomous team actors that are acting as believable characters (e.g., human team actors fall into this category). The idea is that selecting a cast of believable characters can predetermine a solution to an unexpected challenge that the team may be facing in a rescue mission. This approach in certain cases proves more efficient than an alternative approach based on rational decision making and planning, which ignores the question of character believability. This point is illustrated with a simple numerical example in a virtual world paradigm.

The identity of every human subject is imprinted in the subject’s mind. This work explores one possible approach to authentication of the user identity using implicit long-term memory, specifically, memory for faces.

We describe an experiment in using IBM’s Watson cognitive system to teach about human-computer co-creativity in a Georgia Tech Spring 2015 class on computational creativity. The project-based class used Watson to support biologically inspired design, a design paradigm that uses biological systems as analogues for inventing technological systems. The twenty-four students in the class self-organized into six teams of four students each, and developed semester-long projects that built on Watson to support biologically inspired design. In this paper, we describe this experiment in using Watson to teach about human-computer co-creativity, present one project in detail, and summarize the remaining five projects. We also draw lessons on building on Watson for (i) supporting biologically inspired design, and (ii) enhancing human-computer co-creativity.

Assessing the quality of robot social navigation is a challenging problem fraught with human obstacles. From preconceived notions to perspective or point of view, evaluations can differ from person to person. Most work in the field of robot navigation is focused on creating algorithms that produce efficient robot trajectories. We posit that the evaluation of trajectories in a social context is essential and distinct to trajectory generation. In this work we recorded a manually driven powered wheelchair through different scenarios and asked expert evaluators to assess the quality of the powered wheelchair's movement. These evaluations were then compared to post-experiment assessments from trajectory generation algorithms and social navigation concepts. Our results show that it is possible to build a simple model to predict expert evaluators' responses. Unfortunately, there is no clear consensus amongst these experts on what quality behaviour is. This suggests that while current navigation algorithms offer strong heuristics for the generation of smooth trajectories in well-defined environments, their efficacy in evaluating social navigation is less obvious. We believe that more emphasis must be put on dynamic and reactive navigation algorithms as any heuristic approach will be limited due to variance in people's behaviours and expectations.

Issuing and following instructions is a common task in many forms of both human-human and human-robot collaboration. With two human participants, the accuracy of instruction following increases if the collaborators can monitor the state of their partners and respond to them through conversation (Clark and Krych 2004), a process we call social feedback. Despite this benefit in human-human interaction, current human-robot collaboration systems process instructions in non-incremental batches, which can achieve good accuracy but does not allow for reactive feedback (Tellex et al. 2011; Matuszek et al. 2012; Tellex et al. 2012; Misra et al.2014). In this paper, we show that giving a robot the ability to ask the user questions results in responsive conversations and allows the robot to quickly determine the object that the user desires. This social feedback loop between person and robot allows a person to create an internal model for the robot’s mental state and adapt their own behavior to better inform the robot. To close the human-robot feedback loop, we employ a Partially Observable Markov Decision Process (POMDP) to produce a policy which will lead to the determination of the object in the shortest amount of time. To test our approach, we perform user studies to measure our robot’s ability to deliver common household items requested by the participant. We compare delivery speed and accuracy both with and without social feedback.

Recent research has demonstrated that emotion plays a key role in human decision making. Across a wide range of disciplines, old concepts, such as the classical ``rational actor" model, have fallen out of favor in place of more nuanced models (e.g., the frameworks of behavioral economics and emotional intelligence) that acknowledge the role of emotions in analyzing human actions. We now know that context, framing, and emotional and physiological state can all drastically influence decision making in humans. Emotions serve an essential, though often overlooked, role in our lives, thoughts, and decisions. However, it is not clear how and to what extent emotions should impact the design of artificial agents, such as social robots. In this paper I argue that enabling robots, especially those intended to interact with humans, to sense and model emotions will improve their performance across a wide variety of human-interaction applications. I outline two broad research topics (affective inference and learning from affect) towards which progress can be made to enable ``affect-aware" robots and give a few examples of applications in which robots with these capabilities may outperform their non-affective counterparts. By identifying these important problems, both necessary for fully affect-aware social robots, I hope to clarify terminology, assess the current research landscape, and provide goalposts for future research.

This paper presents an exploratory study where participants piloted a commercial UAS (unmanned aerial system) through an obstacle course. The goal was to determine how varying the instructions given to participants affected their performance. Preliminary data suggests future studies to perform, as well as guidelines for human-robot interaction, and some best practices for learning from demonstration studies.

There has been a large body of research demonstrating that students that receive one-on-one tutoring perform, on average, significantly better than students learning via conventional classroom instruction when tested on the same material (Bloom 1984; VanLehn 2011). During tutoring, the teacher has the ability to tailor the instruction and support to the individual learner, creating a personalized learning environment for each student. Research involving robotic agents Figure 1: Child interacting with a NAO robot in a tutoring as tutors indicates that the physical presence of a robot tutor scenario can increase cognitive learning gains (Leyzberg et al. 2010). Further research shows that a robot tutor employing relatively simple personalization strategies can benefit the that on-demand help is useful in interactive learning environments learner (Leyzberg, Spaulding, and Scassellati 2014).

Gaze and gestures are important modalities in human-human interactions and hence important to human-robot interaction. We describe how to use human gaze and robot pointing gestures to disambiguate and extend a human-robot speech dialogue developed for aiding people suffering from dementia.

We report a study performed with a social robot that autonomously plays a competitive game. By relying on an emotional agent architecture (using an appraisal mechanism) the robot was built with the capabilities of emotional appraisal and thus was able to express and share its emotions verbally throughout the game. Contrary to what was expected, emotional sharing in this context seemed to damage the social interaction with the users.