Control devices are essential in shaping the user experience of telepresence robot operators. This is especially true for mobile telemanipulator robots (MTRs), which offer greater opportunities for social interaction compared to mobile or tabletop telepresence robots, but are more difficult to control. This increased difficulty can diminish key aspects of user experience, such as sense of control (SoC), which many users value over performance. However, the usability of telepresence control devices has been critically overlooked in HRI research. In a between-subjects study (n = 63), we investigate how three widely used control devices (mouse, gamepad, haptic controller) affect critical aspects of teleoperation: perceived SoC, safety, usability, and cognitive load; and task performance. Participants remotely controlled an MTR (Stretch) to wait on a customer in a social telepresence setting (cafe). We found that the type of control device significantly impacts both task performance and SoC in direct teleoperation, with the mouse having the best performance/SoC. There were notable tradeoffs in speeds, errors, and task completion times, and participants with higher SoC performed significantly better than those with lower SoC. We provide participant-informed suggestions for future control device design, such as allowing users to reconfigure its physical form, input sensitivity, and controls to align with video game conventions. By foregrounding the role of control devices, our work contributes to ongoing conversations in HRI around the tradeoffs between autonomy, usability, and user agency in social telepresence.

Long Covid (LC) is a debilitating, multisystemic disease that has emerged as the largest mass-disabling event in recent history. Due to the episodic disability and stigma associated with the condition, people with LC (PwLC) often experience social isolation. Mobile telemanipulator robots (MTRs) have the potential to support remote social inclusion for PwLC. However, nuanced MTR design is necessary to accommodate PwLC’s fluctuating symptoms and avoid exacerbating them due to the complexities of teleoperation. In this work, we conducted participatory research with eight PwLC to explore how MTRs can be designed to support their needs. Through online, semi-structured interviews, we found that all participants recognized the potential of MTRs to enhance social inclusion across various settings. Our findings highlight the importance of providing PwLC with adaptive, autonomous support during teleoperation to meet their pacing needs and minimize exertion. Many PwLC preferred MTRs with adjustable autonomy, as they would offer greater agency over the robot’s actions in social spaces. Due to concerns about stigma, participants also wanted MTRs to provide flexible control over the visibility of their disability, allowing them to manage how others perceive them according to their preferences and context. Based on these findings, we present key design considerations, grounded in critical disability studies and critical access studies, for designing MTRs that support remote social inclusion for PwLC while safeguarding their well-being. This work serves as a basis for developing accessible MTR systems that promote inclusivity for PwLC and other chronic conditions.

Gait rehabilitation is a critical aspect of post-stroke recovery, and emerging technologies such as virtual reality and wearables are playing a pivotal role in facilitating this process. However, despite the potential benefits, there is a significant gap in robot-based rehabilitative systems that facilitate repeated use by maintaining users' attention long-term. Our research aims to bridge this gap by creating a comprehensive system that utilizes different feedback types and robotic assistance to support users' gait rehabilitation outcomes. In this paper, we introduce GARRY (Gait Rehabilitation Robotic System), a new robotic system that provides interactive feedback during locomotor training. It promotes engagement by gamifying the rehabilitation process, offering a fun means for the user to meet their rehabilitation goals defined and set by physical therapists. GARRY also incorporates behavioral feedback to introduce a sense of companionship during a session. We make GARRY open-source to other researchers in hopes of encouraging accessibility and to promote research in the field. Our code can be found here: https://github.com/UCSD-RHC-Lab/GARRY