Search for: All records

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in HRI research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this article, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the past 30 years as well as the current directions VAM-HRI is headed in the coming decade. 

Mixed Reality provides a powerful medium for transparent and effective human-robot communication, especially for robots with significant physical limitations (e.g., those without arms). To enhance nonverbal capabilities for armless robots, this article presents two studies that explore two different categories of mixed reality deictic gestures for armless robots: a virtual arrow positioned over a target referent (a non-ego-sensitive allocentric gesture) and a virtual arm positioned over the gesturing robot (an ego-sensitive allocentric gesture). In Study 1, we explore the tradeoffs between these two types of gestures with respect to both objective performance and subjective social perceptions. Our results show fundamentally different task-oriented versus social benefits, with non-ego-sensitive allocentric gestures enabling faster reaction time and higher accuracy, but ego-sensitive gestures enabling higher perceived social presence, anthropomorphism, and likability. In Study 2, we refine our design recommendations by showing that in fact these different gestures should not be viewed as mutually exclusive alternatives, and that by using them together, robots can achieve both task-oriented and social benefits. 

Recently, researchers have initiated a new wave of convergent research in which Mixed Reality visualizations enable new modalities of human-robot communication, including Mixed Reality Deictic Gestures (MRDGs) – the use of visualizations like virtual arms or arrows to serve the same purpose as traditional physical deictic gestures. But while researchers have demonstrated a variety of benefits to these gestures, it is unclear whether the success of these gestures depends on a user’s level and type of cognitive load. We explore this question through an experiment grounded in rich theories of cognitive resources, attention, and multi-tasking, with significant inspiration drawn from Multiple Resource Theory. Our results suggest that MRDGs provide task-oriented benefits regardless of cognitive load, but only when paired with complex language. These results suggest that designers can pair rich referring expressions with MRDGs without fear of cognitively overloading their users. 

We investigate the effectiveness of robot-generated mixed reality gestures. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time, and that robots can pair long referring expressions with mixed reality gestures without cognitively overloading users. 

Mixed Reality visualizations provide a powerful new approach for enabling gestural capabilities on non-humanoid robots. This paper explores two different categories of mixed-reality deictic gestures for armless robots: a virtual arrow positioned over a target referent (a non-ego-sensitive allocentric gesture) and a virtual arm positioned over the gesturing robot (an ego-sensitive allocentric gesture). Specifically, we present the results of a within-subjects Mixed Reality HRI experiment (N=23) exploring the trade-offs between these two types of gestures with respect to both objective performance and subjective social perceptions. Our results show a clear trade-off between performance and social perception, with non-ego-sensitive allocentric gestures enabling faster reaction time and higher accuracy, but ego-sensitive gestures enabling higher perceived social presence, anthropomorphism, and likability. 

To enable robots to select between different types of nonverbal behavior when accompanying spatial language, we must first understand the factors that guide human selection between such behaviors. In this work, we argue that to enable appropriate spatial gesture selection, HRI researchers must answer four questions: (1) What are the factors that determine the form of gesture used to accompany spatial language? (2) What parameters of these factors cause speakers to switch between these categories? (3) How do the parameterizations of these factors inform the performance of gestures within these categories? and (4) How does human generation of gestures differ from human expectations of how robots should generate such gestures? In this work, we consider the first three questions and make two key contributions: (1) a human-human interaction experiment investigating how human gestures transition between deictic and non-deictic under changes in contextual factors, and (2) a model of gesture category transition informed by the results of this experiment. 

We present the first experiment analyzing the effectiveness of robot-generated mixed reality gestures using real robotic and mixed reality hardware. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time during visual search tasks, and show that robots can safely pair longer, more natural referring expressions with mixed reality gestures without worrying about cognitively overloading their interlocutors. 

Situated human-human communication typically involves a combination of both natural language and gesture, especially deictic gestures intended to draw the listener’s attention to target referents. To engage in natural communication, robots must thus be similarly enabled not only to generate natural language, but to generate the appropriate gestures to accompany that language. In this work, we examine the gestures humans use to accompany spatial language, specifically the way that these gestures continuously degrade in specificity and then discretely transition into non-deictic gestural forms along with decreasing confidence in referent location. We then outline a research plan in which we propose to use data collected through our study of this transition to design more human-like gestures for language-capable robots. 

This paper explores the tradeoffs between different types of mixed reality robotic communication under different levels of user workload. We present the results of a within-subjects experiment in which we systematically and jointly vary robot communication style alongside level and type of cognitive load, and measure subsequent impacts on accuracy, reaction time, and perceived workload and effectiveness. Our preliminary results suggest that although humans may not notice differences, the manner of load a user is under and the type of communication style used by a robot they interact with do in fact interact to determine their task effectiveness