ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations

The integration of large language models (LLMs) into conversational robots has made human-robot conversations more dynamic. Yet, LLM-powered conversational robots remain prone to errors, e.g., misunderstanding user intent, prematurely interrupting users, or failing to respond altogether. Detecting and addressing these failures is critical for preventing conversational breakdowns, avoiding task disruptions, and sustaining user trust. To tackle this problem, the ERR@HRI 2.0 Challenge provides a multimodal dataset of LLM-powered conversational robot failures during human-robot conversations and encourages researchers to benchmark machine learning models designed to detect robot failures. The dataset includes 16 hours of dyadic human-robot interactions, incorporating facial, speech, and head movement features. Each interaction is annotated with the presence or absence of robot errors from the system perspective, and perceived user intention to correct for a mismatch between robot behavior and user expectation. Participants are invited to form teams and develop machine learning models that detect these failures using multimodal data. Submissions will be evaluated using various performance metrics, including detection accuracy and false positive rate. This challenge represents another key step toward improving failure detection in human-robot interaction through social signal analysis.

The Design of On-Body Robots for Older Adults

Wearable technology has significantly improved the quality of life for older adults, and the emergence of on-body, movable robots presents new opportunities to further enhance well-being. Yet, the interaction design for these robots remains under-explored, particularly from the perspective of older adults. We present findings from a two-phase co-design process involving 13 older adults to uncover design principles for on-body robots for this population. We identify a rich spectrum of potential applications and characterize a design space to inform how on-body robots should be built for older adults. Our findings highlight the importance of considering factors like co-presence, embodiment, and multi-modal communication. Our work offers design insights to facilitate the integration of on-body robots into daily life and underscores the value of involving older adults in the co-design process to promote usability and acceptance of emerging wearable robotic technologies.

Robot Error Awareness Through Human Reactions: Implementation, Evaluation, and Recommendations

Effective error detection is crucial to prevent task disruption and maintain user trust. Traditional methods often rely on task-specific models or user reporting, which can be inflexible or slow. Recent research suggests social signals, naturally exhibited by users in response to robot errors, can enable more flexible, timely error detection. However, most studies rely on post hoc analysis, leaving their real-time effectiveness uncertain and lacking user-centric evaluation. In this work, we developed a proactive error detection system that combines user behavioral signals (facial action units and speech), user feedback, and error context for automatic error detection. In a study (N = 28), we compared our proactive system to a status quo reactive approach. Results show our system 1) reliably and flexibly detects error, 2) detects errors faster than the reactive approach, and 3) is perceived more favorably by users than the reactive one. We discuss recommendations for enabling robot error awareness in future HRI systems.

Interruption Handling for Conversational Robots

Interruptions, a fundamental component of human communication, can enhance the dynamism and effectiveness of conversations, but only when effectively managed by all parties involved. Despite advancements in robotic systems, state-of-the-art systems still have limited capabilities in handling user-initiated interruptions in real-time. Prior research has primarily focused on post hoc analysis of interruptions. To address this gap, we present a system that detects user-initiated interruptions and manages them in real-time based on the interrupter's intent (i.e., cooperative agreement, cooperative assistance, cooperative clarification, or disruptive interruption). The system was designed based on interaction patterns identified from human-human interaction data. We integrated our system into an LLM-powered social robot and validated its effectiveness through a timed decision-making task and a contentious discussion task with 21 participants. Our system successfully handled 93.69% (n=104/111) of user-initiated interruptions. We discuss our learnings and their implications for designing interruption-handling behaviors in conversational robots.

ERR@HRI 2024 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Interactions

Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions (HRI), encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors and to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.

Designing Social Robots that Engage Older Adults in Exercise: A Case Study

We present and evaluate a prototype social robot to encourage daily exercise among older adults in a home setting. Our prototype system, designed to lead users through exercise sessions with motivational feedback, was assessed through a case study with a 78-year-old participant for one week. Our case study highlighted preferences for greater user control over exercise choices and questioned the necessity of precise motion tracking. Feedback also indicated a desire for more varied exercises and suggested improvements in user engagement techniques. The insights suggest that further research is needed to enhance system adaptability and effectiveness to better promote daily exercise. Future efforts will aim to refine the prototype based on participant feedback and extend the evaluation to broader in-home deployments.

"You Might Like It": How People Respond to Small Talk in Human-Robot Collaboration

In this work, we investigate people's engagement and attitudes towards a non-anthropomorphic robot manipulator that initiates small talk with the user during a collaborative assembly task, and explore how the presence of negative team feedback may affect team dynamics and blame attribution. Through an exploratory study with 20 participants, we found that 18 individuals interacted socially with the robot, nine of which initiated questions back to the robot. We report the frequency and length of users' responses in task-oriented and non-task-oriented dialogue, and further elaborate on people's reactions to the negative system feedback and robot-initiated small talk. We discuss the potential for integrating small talk in non-social robots, and propose three design guidelines to enhance human-robot small talk interactions.

An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research

Quantitative methods in Human-Robot Interaction (HRI) research have primarily relied upon randomized, controlled experiments in laboratory settings. However, such experiments are not always feasible when external validity, ethical constraints, and ease of data collection are of concern. Furthermore, as consumer robots become increasingly available, increasing amounts of real-world data will be available to HRI researchers, which prompts the need for quantative approaches tailored to the analysis of observational data. In this article, we present an alternate approach towards quantitative research for HRI researchers using methods from causal inference that can enable researchers to identify causal relationships in observational settings where randomized, controlled experiments cannot be run. We highlight different scenarios that HRI research with consumer household robots may involve to contextualize how methods from causal inference can be applied to observational HRI research. We then provide a tutorial summarizing key concepts from causal inference using a graphical model perspective and link to code examples throughout the article, which are available at https://gitlab.com/causal/causal_hri. Our work paves the way for further discussion on new approaches towards observational HRI research while providing a starting point for HRI researchers to add causal inference techniques to their analytical toolbox.

Designing AI Support for Human Involvement in AI-assisted Decision Making: A Taxonomy of Human-AI Interactions from a Systematic Review

Efforts in levering Artificial Intelligence (AI) in decision support systems have disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. To address this, explainable AI promotes AI development from a more human-centered perspective. Determining what information AI should provide to aid humans is vital, however, how the information is presented, e. g., the sequence of recommendations and the solicitation of interpretations, is equally crucial. This motivates the need to more precisely study Human-AI interaction as a pivotal component of AI-based decision support. While several empirical studies have evaluated Human-AI interactions in multiple application domains in which interactions can take many forms, there is not yet a common vocabulary to describe human-AI interaction protocols. To address this gap, we describe the results of a systematic review of the AI-assisted decision making literature, analyzing 105 selected articles, which grounds the introduction of a taxonomy of interaction patterns that delineate various modes of human-AI interactivity. We find that current interactions are dominated by simplistic collaboration paradigms and report comparatively little support for truly interactive functionality. Our taxonomy serves as a valuable tool to understand how interactivity with AI is currently supported in decision-making contexts and foster deliberate choices of interaction designs.

Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being "forgetful" of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications.