Fairness is critical for collaboration between humans, and recent research has shown its importance in human–robot collaboration. However, most human-robot interaction (HRI) studies probe fairness judgments toward a robot only at the conclusion of an interaction, overlooking the fact that perceptions of fairness can evolve over time. We present two studies of dynamic fairness that both leverage a Multiplayer Space Invaders game, where a robot controls a spaceship and distributes support across players' sides of the screen. The robot's support is at times biased in favor of one player or the other. In the first study, we examine how fairness perceptions are influenced by the timing of a robot's biased support (early vs. late in the interaction) and the beneficiary of this support (the participant vs. another agent). In the second study, we investigate how expectations of a robot's support behavior (biased vs. unbiased) interact with its actual behavior (biased vs. unbiased) in a setting where two participants each worked to score an individual score threshold. We find that fairness judgments are dynamic: fairness falls after the robot's allocation of support becomes biased but is slower to recover once support becomes unbiased, and participants expecting unbiased behavior judge fairness more harshly when these expectations are violated. Our findings advance understanding of fairness in HRI by presenting it as a dynamic construct shaped not only by the actual behavior of the robot but also by the timing of robot actions and expectations of robot behavior.

Ensuring transparency in robot’s decision-making is increasingly important as they become better at making complex decisions when collaborating with humans. Among the most promising approaches in Human-Robot Interaction (HRI) is the use of counterfactual explanations. Within HRI, counterfactual explanations typically provide insight into robot’s models by presenting changes to the inputs of the model that influence policy decisions and resulting outcomes. While prior work presents counterfactuals that may vary features that users cannot control, we focus on actionable counterfactuals that show how the robot responds to feasible user actions and thereby reveal only what users need to understand to interact effectively. We introduce a novel explanation framework that generates actionable counterfactuals for neural network models in HRI applications. We evaluate this framework in simulation and on live sensor data during an in-person demonstration with groups of participants. Our results highlight the value of each component of our framework and demonstrate its effectiveness in real-time robotic explanations.

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.

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. $250$ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable.