An official website of the United States government
The human-robot interaction (HRI) field has rec- ognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for suc- cessful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-sensitive environments. This study addresses this knowledge gap in an experimental in-lab study to investigate how multimodal robot communication in action teams affects workload and human perception of robots. We explore team collaboration in a medical training scenario where a robotic crash cart (RCC) provides verbal and non-verbal cues to help users remember to perform iterative tasks and search for supplies. Our findings show that verbal cues for object search tasks and visual cues for task reminders reduce team workload and increase perceived ease of use and perceived usefulness more effectively than a robot with no feedback. Our work contributes to multimodal interaction research in the HRI field, highlighting the need for more human-robot teaming research to understand best practices for integrating collaborative robots in time-sensitive environments such as in hospitals, search and rescue, and manufacturing applications.
more »
« less
This content will become publicly available on March 4, 2026
Minding the Stop-Gap: Attending to the “Temporary,” Unplanned, and Added Labor of Human-Robot Collaboration in Context
HRI scholars envision a future of work where human-robot collaboration brings mutual gains: organizations benefit from increased efficiency and productivity, and laborers benefit when tasks are redistributed between humans and robots based on their respective strengths. Yet, ironically, this collaboration in real-world contexts can lead to the opposite effect-workers' efficiency may decrease due to the additional tasks they must undertake to manage unexpected errors caused by robots. This “stop-gap” labor, often viewed as temporary and naturally manageable over time, can have significant and persistent impacts on workers. Drawing from observations across multiple robot deployment sites, this paper highlights the overlooked burden of this labor, challenging idealized visions of seamless human-robot collaboration. We argue that attending to stop-gap labor presents an opportunity for the HRI community to make genuine improvements for workers as primary stakeholders within complex socio-economic networks.
more »
« less
- Award ID(s):
- 2431223
- PAR ID:
- 10650874
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 34 to 44
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Effective interactions between humans and robots are vital to achieving shared tasks in collaborative processes. Robots can utilize diverse communication channels to interact with humans, such as hearing, speech, sight, touch, and learning. Our focus, amidst the various means of interactions between humans and robots, is on three emerging frontiers that significantly impact the future directions of human–robot interaction (HRI): (i) human–robot collaboration inspired by human–human collaboration, (ii) brain-computer interfaces, and (iii) emotional intelligent perception. First, we explore advanced techniques for human–robot collaboration, covering a range of methods from compliance and performance-based approaches to synergistic and learning-based strategies, including learning from demonstration, active learning, and learning from complex tasks. Then, we examine innovative uses of brain-computer interfaces for enhancing HRI, with a focus on applications in rehabilitation, communication, brain state and emotion recognition. Finally, we investigate the emotional intelligence in robotics, focusing on translating human emotions to robots via facial expressions, body gestures, and eye-tracking for fluid, natural interactions. Recent developments in these emerging frontiers and their impact on HRI were detailed and discussed. We highlight contemporary trends and emerging advancements in the field. Ultimately, this paper underscores the necessity of a multimodal approach in developing systems capable of adaptive behavior and effective interaction between humans and robots, thus offering a thorough understanding of the diverse modalities essential for maximizing the potential of HRI.more » « less
-
Abstract As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment (https://uf-retail-cobot-dataset.github.io/). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed.more » « less
-
Human-robot interaction is a critical area of research, providing support for collaborative tasks where a human instructs a robot to interact with and manipulate objects in an environment. However, an under-explored element of these collaborative manipulation tasks are small-scale building exercises, in which the human and robot are working together in close proximity with the same set of objects. Under these conditions, it is essential to ensure the human’s safety and mitigate comfort risks during the interaction. As there is danger in exposing humans to untested robots, a safe and controlled environment is required. Simulation and virtual reality (VR) for HRI have shown themselves to be suitable tools for creating space for human-robot experimentation that can be beneficial in these scenarios. However, the use of simulation and VR comes with the possibility of failures resulting from the sim-to-real gap, where the behavior of the simulated robot may not accurately reflect the experience of a human collaborator in a real-world setting. This gap can limit the generalizability of research findings and raise questions about the validity of using simulation and VR for HRI research. Our goal in this work is to demonstrate the effectiveness of sim-to-real approaches for contact-based human-robot interaction.more » « less
-
A wide range of studies in Human-Robot Interaction (HRI) has shown that robots can influence the social behavior of humans. This phenomenon is commonly explained by the Media Equation. Fundamental to this theory is the idea that when faced with technology (like robots), people perceive it as a social agent with thoughts and intentions similar to those of humans. This perception guides the interaction with the technology and its predicted impact. However, HRI studies have also reported examples in which the Media Equation has been violated, that is when people treat the influence of robots differently from the influence of humans. To address this gap, we propose a model of Robot Social Influence (RoSI) with two contributing factors. The first factor is a robot’s violation of a person’s expectations, whether the robot exceeds expectations or fails to meet expectations. The second factor is a person’s social belonging with the robot, whether the person belongs to the same group as the robot or a different group. These factors are primary predictors of robots’ social influence and commonly mediate the influence of other factors. We review HRI literature and show how RoSI can explain robots’ social influence in concrete HRI scenarios.more » « less
