Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in these settings? We report on two experiments aimed at uncovering users' experiences of an AI agent's participation within a group, in the context of group ideation (brainstorming). In the first study, participants benefited from and preferred having the AI agent in the group, but participants disliked when the agent seemed to dominate the conversation and they desired various controls over its interactive behaviors. In the second study, we created functional controls over the agent's behavior, operable by group members, to validate their utility and probe for additional requirements. Integrating our findings across both studies, we developed a taxonomy of controls for when, what, and where a conversational AI agent in a group should respond, who can control its behavior, and how those controls are specified and implemented. Our taxonomy is intended to aid AI creators to think through important considerations in the design of mixed-initiative conversational agents.

Human–robot proxemics behaviors can vary based on personal, robot, and environmental factors which, along with their deployment in public-facing interactions, calls for an in-depth exploration. This article explores the impact of altitude and safety modifications of small unmanned aerial vehicle (sUAV) on users’ comfortable interaction distance. By leveraging interaction techniques from literature like video, sound, and simulations, we explore personal space interactions in online studies (N = 376) with the sUAV and the Double telepresence robot. We then compare the findings with our in-person interaction data (N = 47). While in-person interactions are the ultimate goal, online methods can be used to reduce resources, allow larger sample sizes, and may lead to a more comprehensive sampling of population than would be expected from in-person studies. The lessons learned from this work are applicable broadly within the social robotics community, even outside those who are interested in proxemics interactions, to conduct future crowd-sourced experiments. The various modalities provided similar trends when compared with data from in-person studies. While the distances may not have been precise compared to those measured in the real world, these experiments are useful to detect patterns in human–robot interactions, and to conduct formative studies before committing resources to in-person testing.

The purpose of this project is to understand how people would expect to interact with an Unmanned Aerial Vehicle (UAV) in a social indoor environment under friendly, neutral, or adversarial contexts. The three environments will include one setting with the UAV serving as a tour guide, one as a security guard, and one as a food delivery mechanism. This work is novel in its inquiry into the affective nature of the interaction, comparison across situational contexts, and ability to compare preferences both within and between participants.Our findings will help researchers plan for appropriate safety and comfort measures, while being cognizant of the participants' preferences for and understanding of how drones operate. This study examines realistic indoor scenarios for which each participant designs their preferred interaction and presents exploratory results, including comparison to prior work with respect to motion gestures and comfortable approach distances. Initial findings suggest the importance of visibility of approaches, selecting approach heights relative to the person and based on the context of interaction, and criticality of the initial direction of motion when classifying the communicative content of UAV flight paths.

Assessing proficiency in small unmanned aerial vehicles (sUAVs) pilots is complex and not well understood, but increasingly important to employ these vehicles in serious jobs such as wildland firefighting and infrastructure inspection. The limited prior work with UAVs has focused on user training using modalities like simulators and VR and no performance assessments with line-of-sight UAVs. This paper presents a training methodology for novice pilots of sUAVs. We presented two studies: the Baseline study (21 participants) and the Training study (16 participants). Our work is of interest to sUAV operators, regulators, and companies developing this technologies to produce a more capable workforce capable of consistent, safe operations. We successfully utilized the method developed in [1] to assess user proficiency in flying UAVs. We presented a UAV pilot training schedule for novice users (in the Training study), and were able to determine the minimum training time necessary to observe performance gains and mitigate damage. Results indicate that task completions noticeably improved and crashes minimized by day 10 of training, with a training plateau observed by day 15.

Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.