Accessible Integration of Physiological Adaptation in Human-Robot Interaction

Technological advancements in creating and commercializing novel unobtrusive wearable physiological sensors have generated new opportunities to develop adaptive human-robot interaction (HRI). Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to creating meaningful interactive experiences. Bodily signals have classically been used for post-interaction analysis in HRI. Despite this, real-time measurements of autonomic responses have been used in other research domains to develop physiologically adaptive systems with great success; increasing user-experience, task performance, and reducing cognitive workload. This thesis presents the HRI Physio Lib, a conceptual framework, and open-source software library to facilitate the development of physiologically adaptive HRI scenarios. Both the framework and architecture of the library are described in-depth, along with descriptions of additional software tools that were developed to make the inclusion of physiological signals easier for robotics frameworks. The framework is structured around four main components for designing physiologically adaptive experimental scenarios: signal acquisition, processing and analysis; social robot and communication; and scenario and adaptation. Open-source software tools have been developed to assist in the individual creation of each described component. To showcase our framework and test the software library, we developed, as a proof-of-concept, a simple scenario revolving around a physiologically aware exercise coach, that modulates the speed and intensity of the activity to promote an effective cardiorespiratory exercise. We employed the socially assistive QT robot for our exercise scenario, as it provides a comprehensive ROS interface, making prototyping of behavioral responses fast and simple. Our exercise routine was designed following guidelines by the American College of Sports Medicine. We describe our physiologically adaptive algorithm and propose an alternative second one with stochastic elements. Finally, a discussion about other HRI domains where the addition of a physiologically adaptive mechanism could result in novel advances in interaction quality is provided as future extensions for this work. From the literature, we identified improving engagement, providing deeper social connections, health care scenarios, and also applications for self-driving vehicles as promising avenues for future research where a physiologically adaptive social robot could improve user experience

Exploring the Verifiability of Code Generated by GitHub Copilot

GitHub's Copilot generates code quickly. We investigate whether it generates good code. Our approach is to identify a set of problems, ask Copilot to generate solutions, and attempt to formally verify these solutions with Dafny. Our formal verification is with respect to hand-crafted specifications. We have carried out this process on 6 problems and succeeded in formally verifying 4 of the created solutions. We found evidence which corroborates the current consensus in the literature: Copilot is a powerful tool; however, it should not be "flying the plane" by itself.Comment: HATRA workshop at SPLASH 202

Expectations Vs. Reality: Unreliability and Transparency in a Treasure Hunt Game With Icub

Trust is essential in human-robot interactions, and in times where machines are yet to be fully reliable, it is important to study how robotic hardware faults can affect the human counterpart. This experiment builds on a previous research that studied trust changes in a game-like scenario with the humanoid robot iCub. Several robot hardware failures (validated in another online study) were introduced in order to measure changes in trust due to the unreliability of the iCub. A total of 68 participants took part in this study. For half of them, the robot adopted a transparent approach, explaining each failure after it happened. Participants' behaviour was also compared to the 61 participants that played the same game with a fully reliable robot in the previous study. Against all expectations, introducing manifest hardware failures does not seem to significantly affect trust, while transparency mainly deteriorates the quality of interaction with the robot