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An overarching goal of Artificial Intelligence (AI) is creating autonomous, social agents that help people. Two important challenges, though, are that different people prefer different assistance from agents and that preferences can change over time. Thus, helping behaviors should be tailored to how an individual feels during the interaction. We hypothesize that human nonverbal behavior can give clues about users' preferences for an agent's helping behaviors, augmenting an agent's ability to computationally predict such preferences with machine learning models. To investigate our hypothesis, we collected data from 194 participants via an online survey in which participants were recorded while playing a multiplayer game. We evaluated whether the inclusion of nonverbal human signals, as well as additional context (e.g., via game or personality information), led to improved prediction of user preferences between agent behaviors compared to explicitly provided survey responses. Our results suggest that nonverbal communication -- a common type of human implicit feedback -- can aid in understanding how people want computational agents to interact with them. 

We propose a demonstration of the Social Environment for Autonomous Navigation with Virtual Reality (VR) for advancing research in Human-Robot Interaction. In our demonstration, a user controls a virtual avatar in simulation and performs directed navigation tasks with a mobile robot in a warehouse environment. Our demonstration shows how researchers can leverage the immersive nature of VR to study robot navigation from a user-centered perspective in densely populated environments while avoiding physical safety concerns common with operating robots in the real world. This is important for studying interactions with robots driven by algorithms that are early in their development lifecycle. 

Recent research in robot learning suggests that implicit human feedback is a low-cost approach to improving robot behavior without the typical teaching burden on users. Because implicit feedback can be difficult to interpret, though, we study different methods to collect fine-grained labels from users about robot performance across multiple dimensions, which can then serve to map implicit human feedback to performance values. In particular, we focused on understanding the effects of annotation order and frequency on human perceptions of the self-annotation process and the usefulness of the labels for creating data-driven models to reason about implicit feedback. Our results demonstrate that different annotation methods can influence perceived memory burden, annotation difficulty, and overall annotation time. Based on our findings, we conclude with recommendations to create future implicit feedback datasets in Human-Robot Interaction. 

Much prior work on creating social agents that assist users relies on preconceived assumptions of what it means to be helpful. For example, it is common to assume that a helpful agent just assists with achieving a user’s objective. However, as assistive agents become more widespread, human-agent interactions may be more ad-hoc, providing opportunities for unexpected agent assistance. How would this affect human notions of an agent’s helpfulness? To investigate this question, we conducted an exploratory study (N=186) where participants interacted with agents displaying unexpected, assistive behaviors in a Space Invaders game and we studied factors that may influence perceived helpfulness in these interactions. Our results challenge the idea that human perceptions of the helpfulness of unexpected agent assistance can be derived from a universal, objective definition of help. Also, humans will reciprocate unexpected assistance, but might not always consider that they are in fact helping an agent. Based on our findings, we recommend considering personalization and adaptation when designing future assistive behaviors for prosocial agents that may try to help users in unexpected situations. 

While neural network binary classifiers are often evaluated on metrics such as Accuracy and F1-Score, they are commonly trained with a cross-entropy objective. How can this training-evaluation gap be addressed? While specific techniques have been adopted to optimize certain confusion matrix based metrics, it is challenging or impossible in some cases to generalize the techniques to other metrics. Adversarial learning approaches have also been proposed to optimize networks via confusion matrix based metrics, but they tend to be much slower than common training methods. In this work, we propose a unifying approach to training neural network binary classifiers that combines a differentiable approximation of the Heaviside function with a probabilistic view of the typical confusion matrix values using soft sets. Our theoretical analysis shows the benefit of using our method to optimize for a given evaluation metric, such as F1-Score, with soft sets, and our extensive experiments show the effectiveness of our approach in several domains. 

We study two approaches for predicting an appropriate pose for a robot to take part in group formations typical of social human conversations subject to the physical layout of the surrounding environment. One method is model-based and explicitly encodes key geometric aspects of conversational formations. The other method is data-driven. It implicitly models key properties of spatial arrangements using graph neural networks and an adversarial training regimen. We evaluate the proposed approaches through quantitative metrics designed for this problem domain and via a human experiment. Our results suggest that the proposed methods are effective at reasoning about the environment layout and conversational group formations. They can also be used repeatedly to simulate conversational spatial arrangements despite being designed to output a single pose at a time. However, the methods showed different strengths. For example, the geometric approach was more successful at avoiding poses generated in nonfree areas of the environment, but the data-driven method was better at capturing the variability of conversational spatial formations. We discuss ways to address open challenges for the pose generation problem and other interesting avenues for future work. 

We study conversational group detection in varied social scenes using a message-passing Graph Neural Network (GNN) in combination with the Dominant Sets clustering algorithm. Our approach first describes a scene as an interaction graph, where nodes encode individual features and edges encode pairwise relationship data. Then, it uses a GNN to predict pairwise affinity values that represent the likelihood of two people interacting together, and computes non-overlapping group assignments based on these affinities. We evaluate the proposed approach on the Cocktail Party and MatchNMingle datasets. Our results suggest that using GNNs to leverage both individual and relationship features when computing groups is beneficial, especially when more features are available for each individual.