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* 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 [...]

* 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 [...]

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, [...]

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.

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.