As robots evolve into collaborators in human-robot teams, appropriately calibrated trust becomes crucial. This study investigates trust dynamics in an Augmented Reality-based human-robot teaming system, focusing on the interplay between robot performance and robot-to-human feedback. In an experiment with 32 participants, we examined how robot feedback influences user trust, particularly when it is mismatched with robot performance. The results show that while robot-to-human feedback does not significantly affect trust on its own, it positively affects user responses when matched to performance. Robot performance had a stronger influence on trust than feedback, and error severity significantly impacted trust levels. These findings contribute to understanding trust calibration in human-robot interactions and provide insight for designing effective trust-aware robotic systems, addressing critical gaps in existing research, and offering implications for improving human-robot collaboration across various domains.

The creation of information transparency solutions to enable humans to understand robot perception is a challenging requirement for autonomous and artificially intelligent robots to impact a multitude of domains. By taking advantage of comprehensive and high-volume data from robot teammates’ advanced perception and reasoning capabilities, humans will be able to make better decisions, with significant impacts from safety to functionality. We present a solution to this challenge by coupling augmented reality (AR) with an intelligent mobile robot that is autonomously detecting novel changes in an environment. We show that the human teammate can understand and make decisions based on information shared via AR by the robot. Sharing of robot-perceived information is enabled by the robot’s online calculation of the human’s relative position, making the system robust to environments without external instrumentation such as global positioning system. Our robotic system performs change detection by comparing current metric sensor readings against a previous reading to identify differences. We experimentally explore the design of change detection visualizations and the aggregation of information, the impact of instruction on communication understanding, the effects of visualization and alignment error, and the relationship between situated 3D visualization in AR and human movement in the operational environment on shared situational awareness in human-robot teams. We demonstrate this novel capability and assess the effectiveness of human-robot teaming in crowdsourced data-driven studies, as well as an in-person study where participants are equipped with a commercial off-the-shelf AR headset and teamed with a small ground robot that maneuvers through the environment. The mobile robot scans for changes, which are visualized via AR to the participant. The effectiveness of this communication is evaluated through accuracy and subjective assessment metrics to provide insight into interpretation and experience.

Robot designers and Human-Robot Interaction (HRI) practitioners can face challenges when people form a mental model of a robot that is not appropriate. Although the field of robotics would benefit significantly from a broad representation of designers, there is currently no comprehensive method of including many people in the design process and no theory of what expectations a robot design feature might elicit. We seek to address these challenges through the creation of a robot design platform, an online tool similar to a character creation interface in a video game, where users create a robot design. By collecting a large number of robot designs from users, we seek to be able to identify aspects of a robot’s design that influence the mental models humans ascribe to the robot. To maximize the universal usability of the platform, we conducted a three-part survey to assess which icons should be used to visually represent the mental states ascribed to the robots created by users on the platform. In our assessment, we found nine icons that met our criteria for use in the platform and others that should be further evaluated.

This work presents our strategy for investigating the fundamental guidelines and theories related to robot mind perception, and for establishing a metric for mental models, using our web-based tool, Build-A-Bot. We also discuss the effectiveness and efficiency of our platform by virtue of its inclusive design and its ability to visualize the user’s intended representation of a mental model for a robot through a 3D game-like interface. We conducted an observational user test study to assess if the website and the embedded robot building tool are effective and efficient to use for users. We found that the design of the robot creation platform and its associated website are considered intuitive and effective by a majority of our survey population. The Build-A-Bot platform successfully provides the ability for users to visualize their ideal representation of their mental model through an interactive game. Based on the obtained data, we propose further steps to optimize the Build-A-Bot platform for universal usability

A common issue in Human-Robot Interaction is a gap in understanding how robot designs are perceived by the user. A common issue encountered by practitioners of Machine Learning (ML) is a lack of salient data to use in training. The “Build-A-Bot” project is developing a novel research platform implemented as a web-accessible 3D game that affords data collection of many user-provided robot designs. The designs are used to train ML models to better evaluate robot designs, predict how a design will be perceived using Convolutional Neural Networks (CNNs), and create new robot designs using Generative Adversarial Networks (GANs). This paper outlines the current and future work accomplished by an interdisciplinary undergraduate student team at the University of Denver across Computer Science, Music, Psychology, and other related STEM fields that have created Build-A-Bot.