arXiv subjects

Kate Candon

Publications and source records attributed to Kate Candon.

4 recordsLinked to original sources

arXiv2025

The Social Context of Human-Robot Interactions

The Human-Robot Interaction (HRI) community often highlights the social context of an interaction as a key consideration when designing, implementing, and evaluating robot behavior. Unfortunately, researchers use the term "social context" in varied ways. This can lead to miscommunication, making it challenging to draw connections between related work on understanding and modeling the social contexts of human-robot interactions. To address this gap, we survey the HRI literature for existing definitions and uses of the term "social context". Then, we propose a conceptual model for describing the social context of a human-robot interaction. We apply this model to existing work, and we discuss a range of attributes of social contexts that can help researchers plan for interactions, develop behavior models for robots, and gain insights after interactions have taken place. We conclude with a discussion of open research questions in relation to understanding and modeling the social contexts of human-robot interactions.

cs.RO↗

arXiv2024

Dynamic Fairness Perceptions in Human-Robot Interaction

People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However, such an approach is limited as interactions vary over time, potentially causing changes in fairness perceptions as well. To validate this idea, we conducted a 2x2 user study with a mixed design (N=40) where we investigated two factors: the timing of unfair robot actions (early or late in an interaction) and the beneficiary of those actions (either another robot or the participant). Our results show that fairness judgments are not static. They can shift based on the timing of unfair robot actions. Further, we explored using perceptions of three key factors (reduced welfare, conduct, and moral transgression) proposed by a Fairness Theory from Organizational Justice to predict momentary perceptions of fairness in our study. Interestingly, we found that the reduced welfare and moral transgression factors were better predictors than all factors together. Our findings reinforce the idea that unfair robot behavior can shape perceptions of group dynamics and trust towards a robot and pave the path to future research directions on moment-to-moment fairness perceptions

cs.RO↗

arXiv2024

REACT: Two Datasets for Analyzing Both Human Reactions and Evaluative Feedback to Robots Over Time

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressions, or body motions that reflect internal human states. To facilitate future research in this direction, we contribute the REACT database, a collection of two datasets of human-robot interactions that display users' natural reactions to robots during a collaborative game and a photography scenario. Further, we analyze the datasets to show that interaction history is an important factor that can influence human reactions to robots. As a result, we believe that future models for interpreting implicit feedback in HRI should explicitly account for this history. REACT opens up doors to this possibility in the future.

cs.RO↗

arXiv2020

Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

While neural network binary classifiers are often evaluated on metrics such as Accuracy and $F_1$-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 $F_1$-Score, with soft sets, and our extensive experiments show the effectiveness of our approach in several domains.

cs.LG↗