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This article is an introduction to a special issue of the ACM Transactions on Human-Robot Interaction (ACM THRI) that focuses on the interface between artificial intelligence (AI) and human-robot interaction (HRI). The issue includes 11 articles that cover a wide range of topics, including teleoperation, shared control, teaching robots, monitoring autonomous robots, augmented reality interfaces, predicting team motions, planning for adaptation in collaborative tasks, automating gesture generation, and the impact of vulnerability on trust in HRI. The articles present innovative computational techniques and systems that demonstrate the state-of-the-art in AI-HRI research. [Extracted from the article]

The proliferation of Large Language Models (LLMs) presents both a critical design challenge and a remarkable opportunity for the field of Human–Robot Interaction (HRI). While the direct deployment of LLMs on interactive robots may be unsuitable for reasons of ethics, safety, and control, LLMs might nevertheless provide a promising baseline technique for many elements of HRI. Specifically, in this article, we argue for the use of LLMs as Scarecrows: "brainless," straw-man black-box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions, much like the use of "Wizard of Oz" (WoZ) and other human-in-the-loop approaches. We explicitly acknowledge that these Scarecrows, rather than providing a satisfying or scientifically complete solution, incorporate a form of the wisdom of the crowd and, in at least some cases, will ultimately need to be replaced or supplemented by a robust and theoretically motivated solution. We provide examples of how Scarecrows could be used in language-capable robot architectures as useful placeholders and suggest initial reporting guidelines for authors, mirroring existing guidelines for the use and reporting of WoZ techniques. [ABSTRACT FROM AUTHOR]

Due to their quantitative nature, robotic systems are useful tools for systematically augmenting human behavior and performance in dynamic environments, such as therapeutic rehabilitation settings. The efficacy of human-robot interaction (HRI) in these settings will depend on the robot’s coaching style. Our goal was to investigate the influence of robot coaching styles designed to enhance motivation and encouragement on post-stroke individuals during motor task practice. We hypothesized that coaching styles incorporating user performance and preference would be preferred in a therapeutic HRI setting. We designed an evaluation study with seven individuals post stroke. A socially assistive robotics (SAR) system using three different coaching styles guided participants during performance of an upper extremity practice task. User preference was not significantly affected by the different robot coaching styles in our participant sample (H(2) = 2.638, p = 0.267). However, trends indicated differences in preference for the coaching styles. Our results provide insights into the design and use of SAR systems in therapeutic interactions aiming to influence user behavior.

This special issue of the Transactions on Human-Robot Interaction highlights, documents, and explores the metrics, test methods, and artifacts used in human-robot interaction (HRI) research. This collection of articles brings to attention the commonalities between the application of measurement science for the assessment and assurance of human-centric robotics in a variety of application domains, including industry, education, and defense. This special issue draws specific attention to the use and impact of metrology toward the advancement of HRI technologies and algorithms, and it promotes the application of measurement science toward the benchmarking and replication of HRI research. Special attention is given to the use cases, data sets, test methodologies, measurement techniques, metrics, and statistical analyses used to evaluate system performance. [ABSTRACT FROM AUTHOR]

On March 27–29, 2023, the AAAI symposium on "HRI in Academia and Industry: Bridging the Gap" was held in a hybrid format, with both in‐person and remote participants, gathering Human‐Robot Interaction (HRI) researchers and practitioners from academia, industry, and national research laboratories to find common ground, understand the different constraints at play, and determine how to work together. The use of robots that operate in spaces in which humans are physically co‐present is growing at a dramatic rate. We are seeing more and more robots in our warehouses, on our streets, and even in our homes. All of these robots will interact with humans in some way, whether intentionally or unintentionally. To be successful, their interactions with humans will have to be carefully designed. For more than a decade, the field of HRI has been growing at the intersection of robotics, Artificial Intelligence (AI) , human‐computer interaction (HCI), psychology, and other fields; however, until quite recently, it has been a largely academic area, with university researchers proposing, implementing, and reporting on experiments at a limited scale. With the current increase of commercially‐available robots, HRI is starting to make its way into the robotics industry in a meaningful way. This symposium brought together HRI researchers and practitioners from academia, industry, and national research laboratories to find common ground, understand the different constraints at play, and determine how to effectively work together. [ABSTRACT FROM AUTHOR]

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system—design, hardware, and software—for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human–robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform. [ABSTRACT FROM AUTHOR]

The AAAI 2018 Fall Symposium Series was held Thursday through Saturday, October 18-20, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, D.C. The titles of the eight symposia were Adversary-Aware Learning Techniques and Trends in Cybersecurity; Artificial Intelligence for Synthetic Biology; Artificial Intelligence in Government and Public Sector; A Common Model of Cognition; Gathering for Artificial Intelligence and Natural System; Integrating Planning, Diagnosis, and Causal Reasoning; Interactive Learning in Artificial Intelligence for Human-Robot Interaction; and Reasoning and Learning in Real-World Systems for Long-Term Autonomy. The highlights of each symposium (except the Gathering for Artificial Intelligence and Natural System symposium, whose organizers failed to submit a report) are presented in this report. [ABSTRACT FROM AUTHOR]

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17-19, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction; Cognitive Assistance in Government and Public Sector Applications; Cross-Disciplinary Challenges for Autonomous Systems; Privacy and Language Technologies; and Shared Autonomy in Research and Practice. The highlights of each symposium (except the Accelerating Science symposium) are presented in this report. [ABSTRACT FROM AUTHOR]

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016, at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics, and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in So - cially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Nonhuman Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through So - cial Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science. [ABSTRACT FROM AUTHOR]

We present an application of a socially assistive robotics (SAR) system in a therapeutic setting. We examine the amount of interaction elicited by the robot in a therapeutic setting with individuals post-stroke. We examine the role of various communication modalities, and their affects on the participants' responses. Seven participants of mild to moderate functional impairment due to stroke interacted with our SAR system during three sessions of motor task practice. The robot guided the users as they performed a wire puzzle task, while providing them with feedback about their performance. We evaluated the amount of verbalization and eye contact made with the robot. Our results indicate that users make eye contact more often than they verbalize when interacting with the robot. Further, user interactions are most frequent at the beginning of a practice session, and occur less frequently as the session progresses. When a user observes that the robot is not responding to a certain type of communication, the user limits the use of that communication modality. These insights should be useful in the design of future robot-based therapeutic interventions., (© 2011 IEEE)