The 8th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) seeks to bring together researchers from human-robot interaction (HRI) and human-robot collaboration (HRC), robotics, and mixed reality (MR) to address the challenges related to MR interactions between humans, robots, and agents. Key topics include the development of robots capable of interacting with humans in MR, the use of virtual reality for creating interactive robots, designing augmented reality interfaces for communication and control between humans and robots, exploring MR interfaces for enhancing robot learning and collaboration, comparative analysis of the capabilities and perceptions of robots and virtual agents, and sharing best design practices. VAM-HRI-2025 will build on the success of VAM-HRI workshops held from 2018 to 2024, advancing research in this specialized community.

As robots continue to be adopted into our everyday lives they may encounter unforeseen circumstances, resulting in failures that require assistance from nearby people.When people enter interactions with robots, they leverage their mental models of the system and its functions.These mental models are based on a person's knowledge of and experiences with that robot and others.For this reason, the models are often incomplete or inaccurate, resulting in inefficient interactions.Understanding a complex robot and its functions is difficult, especially for novices.Therefore, when robots require assistance it is necessary for them to explain their failures in a manner that not only provides enough context for a person to resolve the error, but that also helps correct people's misaligned mental models.Through this work, I aim to enable non-experts to more efficiently and effectively diagnose and resolve robot failures.

As robots tackle increasingly complex tasks, the need for explanations becomes essential for gaining trust and acceptance. Explainable robotic systems should not only elucidate failures when they occur but also predict and preemptively explain potential issues. This paper compares explanations from Reactive Systems, which detect and explain failures after they occur, to Proactive Systems, which predict and explain issues in advance. Our study reveals that the Proactive System fosters higher perceived intelligence and trust and its explanations were rated more understandable and timely. Our findings aim to advance the design of effective robot explanation systems, allowing people to diagnose and provide assistance for problems that may prevent a robot from finishing its task.

The need for robots to explain their failures grows as the variety and number of robots deployed in public, homes, and work environments increases. This paper extends our prior work utilizing explanation templates by comparing those Templated explanations to Generative explanations created by a Large Language Model. Our study surprisingly reveals that Templated explanations result in similar or higher perceived intelligence and trust while also being more understandable. Through our findings, we aim to provide considerations for effective robot explanation systems, ultimately enabling people to be able to understand and provide assistance to robots that have encountered unforeseen circumstances.

Human-in-the-loop robot teleoperation interfaces enable operators to control robots to complete complex tasks, as seen by the success of teams in the DARPA Robotics Challenge (DRC). In this work, we compare two human-in-the-loop planning interfaces, a 2D keyboard and mouse (KBM) interface modeled after those used in the DRC and a 3D virtual reality (VR) interface, for teleoperating a robot to perform navigation and manipulation tasks. In our study, we investigated operator performance, and cognitive workload while using the interface, as well as the perceived usability of each. We found that participants had better performance in both task types when using the KBM interface, however they experienced fewer collisions between the robot and the world in the VR interface. Given these findings, we recommend utilizing a KBM interface in low-risk situations where task performance is the primary factor. In high-risk scenarios, where collisions can be detrimental, we recommend using VR. With this work we aim to contribute to building effective and intuitive interfaces for human-in-the-loop planning to allow robots to complete complex tasks in challenging environments.

Human–robot collaboration is becoming increasingly common in factories around the world; accordingly, we need to improve the interaction experiences between humans and robots working in these spaces. In this article, we report on a user study that investigated methods for providing information to a person about a robot’s intent to move when working together in a shared workspace through signals provided by the robot. In this case, the workspace was the surface of a tabletop. Our study tested the effectiveness of three motion-based and three light-based intent signals as well as the overall level of comfort participants felt while working with the robot to sort colored blocks on the tabletop. Although not significant, our findings suggest that the light signal located closest to the workspace—an LED bracelet located closest to the robot’s end effector—was the most noticeable and least confusing to participants. These findings can be leveraged to support human–robot collaborations in shared spaces.

The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrated task sets, irregular objects, confined space, and machining, introducing new challenges in the mobile manipulation domain. Here we describe our efforts to address these challenges by demonstrating the assembly of a kit of mechanical parts in a caddy. In addition to implementation details, we examine the issues in this task set extensively, and we discuss our software architecture in the hope of providing a base for other researchers. To evaluate performance and consistency, we conducted 20 full runs, then examined failure cases with possible solutions. We conclude by identifying future research directions to address the open challenges.