There is significant interest in enabling robots to learn to perform tasks directly from interactions with non-expert users. Typically, a human serves as a teacher whose only task is to provide feedback to a robot learner. However, in real-world human-robot collaborations, the human often assists with the task while also offering feedback. Our key insight is that we can extract additional, implicit feedback from the human’s actions during the collaboration to augment the robot learning process. Under the assumption of fixed-role assignments, we first propose to formalize human preferences over a human-robot collaboration as a shared set of parameters encoding alignment between two reward functions: one that drives human behavior, and another that should direct robot behavior. This allows us to extract implicit feedback from an interaction by reasoning about the human’s actions in the task as actions that reveal the human’s preferences. Then, we combine this implicit feedback with traditional explicit human feedback to facilitate estimating the human’s preferences. We evaluated our proposed approach for Preference learning from Implicit and Explicit feedback (PIE) in simulations and with real users in a cooking scenario. Our simulation results indicate that combining multiple modalities of human feedback improves a robot’s ability to estimate human preferences over the collaboration, with a similar trend observed in real-world evaluations. These findings highlight a promising direction for enabling robots to adapt to a user’s preference model more quickly, thereby reducing the amount of time a person must spend teaching a robot.

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.

Determining whether a robot's actions will be perceived as fair or unfair is complicated in Human-Robot Interaction (HRI), where factors like the robot's perceived agency and intent may influence these judgments. We report findings from two experiments that examine how people evaluate fairness after reviewing a scenario where a robot harms a human. In these experiments, we manipulate different aspects of the context: the fairness of a situation (Fair vs. Unfair); the perceived agency of a robot that commits the harm (High Agency vs. Low Agency); and the perceived intention behind the harmful action (Intentional vs. Unintentional). We examine fairness as a multifaceted construct, using Fairness Theory to capture three key components: reduced welfare, conduct, and moral transgression. We find that this multifaceted perspective can capture nuances in fairness judgments. When robots are perceived to have greater decision-making autonomy, humans tend to assign higher moral responsibility, especially when harmful actions appear intentional. Conversely, when robots are seen as merely following predetermined programming, people focus more on the possibility that the programming could have been designed differently. These findings highlight how agency and intention need to be considered when investigating fairness in HRI.

Prior work has shown that humans are acutely aware of unfair treatment and that fairness is critical for sustaining collaboration. This makes determining whether a robot’s behavior is perceived as fair or unfair important in Human-Robot Interaction (HRI). Traditionally, this is achieved via surveys through which people provide their opinion of a robot. However, with advancements in Large Language Models (LLMs) and significant efforts to support AI alignment, we hypothesized that LLMs could help determine human perceptions of fairness in a variety of HRI scenarios in a way that resembles human judgments. We first investigated how effective several LLMs were in inferring human fairness judgments in scenarios where unfair or fair outcomes came about due to a robot’s behavior. Then, we compared the open-ended justifications that LLMs and humans provided. Our results suggest that LLM-generated fairness ratings align well with the directionality of human survey responses. However, the justifications that humans provided for their responses differed from LLM justifications. These findings highlight both the potential and limitations of using LLMs to approximate human fairness judgments. Ultimately, this work lays the foundation for enabling robots to autonomously reflect on their behavior through a fairness lens, paving the way for more ethically aligned human-robot collaborations.

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.

As robots become more ubiquitous across human spaces, it is becoming increasingly relevant for researchers to ask the question, “how can we ensure that we are designing robots to be sufficiently equipped to treat people fairly?”. This workshop brings together researchers across the fields of Human-Robot Interaction (HRI), fairness in machine learning, design, and transparency in AI to shed light on the relevant methodological challenges surrounding issues of fairness and transparency in HRI. In our workshop, we will attempt to identify synergies between these various fields. In particular, we will focus on how HRI can leverage these existing rich body of work to guide the formalization of fairness metrics and methodologies. Another goal of the workshop is to foster a community of interdisciplinary researchers to encourage collaboration. The complexity in defining fairness lies in its context sensitive nature, as such we look to the influx of definitions from the field of fairness in artificial intelligence, design, and organizational psychology to derive a set of definitions that could serve as guidelines for researchers in HRI.

We propose the use of interactive vignettes as an alternative to traditional text- and video-based vignettes for conducting large-scale Human-Robot Interaction (HRI) studies. Interactive vignettes maintain the advantages of traditional vignettes while offering additional affordances for participant interaction and data collection through interactive elements. We discuss the core affordances of interactive vignettes, including explorability, responsiveness, and non-linearity, and look into how these affordances can enable HRI research with more complex scenarios. To demonstrate the strength of the approach, we present a case study of our own research project with N=87 participants and show the data we collect through interactive vignettes. We suggest that the use of interactive vignettes can benefit HRI researchers in learning how participants interact with, respond to, and perceive a robot’s behavior in pre-defined scenarios.

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should allocate resources among group members and about what the consequences of such allocation are for a group’s social dynamics and outcomes. Methodological advances are needed to answer these questions allow researchers to collect data about a robot’s impact not only on interactions with the robot but also on interactions of people with each other. This paper presents Robot Assisted Tower Construction, a novel task that allows researchers to examine the impact of a robot’s allocation behavior on the dynamics of a group or team collaborating on a task. By focusing on the question of whether and how a robot’s allocation of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, a case is provided of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves a group of people. We highlight the kinds of insights the task can yield and how it can be adapted to various human robot collaboration contexts.

How should a robot that collaborates with multiple people decide upon the distribution of resources (e.g. social attention, or parts needed for an assembly)? People are uniquely attuned to how resources are distributed. A decision to distribute more resources to one team member than another might be perceived as unfair with potentially detrimental effects for trust. We introduce a multi-armed bandit algorithm with fairness constraints, where a robot distributes resources to human teammates of different skill levels. In this problem, the robot does not know the skill level of each human teammate, but learns it by observing their performance over time. We define fairness as a constraint on the minimum rate that each human teammate is selected throughout the task. We provide theoretical guarantees on performance and perform a large-scale user study, where we adjust the level of fairness in our algorithm. Results show that fairness in resource distribution has a significant effect on users' trust in the system.

Interpersonal distance behaviors can vary significantly across countries and impact human social interaction. Do these cross-cultural differences play out when one of the interaction partners participates through a teleoperated robot? Emerging research shows that when being approached by a robot, people tend to hold similar cultural preferences as they would for an approaching human. However, no work yet has investigated this question from a robot teleoperator's perspective. Toward answering this, we conducted an online study (N = 774) using a novel simulation paradigm across two countries (U.S. and India). Results show that in the role of a telepresence robot operator, participants exhibited cross-cultural differences in interpersonal distance behavior in line with human-human proxemic research, indicating that culture-specific distance behavior can manifest in the way a robot operator controls a robot. We discuss implications for designers who seek to automate path planning and navigation for teleoperated robots.