Millions of individuals worldwide experience signif-icant disability, often relying on caregivers for activities of daily living such as eating, bathing, ambulating, and dressing. This reliance on caregivers can negatively impact their mental health and can place a considerable physical workload on caregivers. Physical robot caregiving has emerged as a promising solution to this challenge, with recent years seeing a surge of research interest in developing physically assistive robots for people with disabilities. This workshop focuses on bringing together researchers, end-users, caregivers, and healthcare professionals to discuss existing research on physical caregiving robots, identify gaps, foster collaborations, and chart future research directions.

Millions of people cannot eat independently due to a disability, and caregiver-assisted meals can make them feel self-conscious, pressured, or burdensome. Robot-assisted feeding promises to empower people with motor impairments to feed themselves. However, current research typically examines specific robotic system subcomponents and evaluates them in controlled lab settings. This leaves a gap in developing and evaluating an end-to-end system that can feed entire meals in out-of-lab settings. We present one such system, which we developed collaboratively with two community researchers (CRs) with motor-impairments. The key challenge of developing a robot feeding system for out-of-lab use is the varied off-nominal scenarios that inevitably arise. Our key insight is that users can overcome many off-nominals, provided customizability and control over the system. Our system improves upon the state-of-the-art with: (1) a user interface that provides substantial user customizability and control, (2) a bite selection implementation that incorporates users-in-the-loop to generalize across food items, and (3) portable hardware that facilitates system use in diverse environments without inhibiting user mobility. We conduct two studies to evaluate the system. In Study 1, five users with motor impairments and one CR use the system to feed themselves meals of their choice in a cafeteria, office, or conference room. In Study 2, one CR uses the system in his home for five days, feeding himself 10 meals across diverse contexts. We present 3 key lesson learned: (1) spatial contexts are numerous, customizability lets users adapt to them; (2) off-nominals will arise, variable autonomy lets users overcome them; and (3) assistive robots' benefits depend on context. We provide video footage and code on our website.

Robot-assisted bite acquisition involves picking up food items with varying shapes, compliance, sizes, and textures. Fully autonomous strategies may not generalize efficiently across this diversity. We propose leveraging feedback from the care recipient when encountering novel food items. However, frequent queries impose a workload on the user. We formulate human-in-the-loop bite acquisition within a contextual bandit framework and introduce LINUCB-QG, a method that selectively asks for help using a predictive model of querying workload based on query types and timings. This model is trained on data collected in an online study involving 14 participants with mobility limitations, 3 occupational therapists simulating physical limitations, and 89 participants without limitations. We demonstrate that our method better balances task performance and querying workload compared to autonomous and always-querying baselines and adjusts its querying behavior to account for higher workload in users with mobility limitations. We validate this through experiments in a simulated food dataset and a user study with 19 participants, including one with severe mobility limitations. Please check out our project website at: emprise.cs.cornell.edu/hilbiteacquisition/.

Social dining, i.e., eating with/in company, is replete with meaning and cultural significance. Unfortunately, for the 1.8 million Americans with motor impairments who cannot eat without assistance, challenges restrict them from enjoying this pleasant social ritual. In this work, we identify the needs of participants with motor impairments during social dining and how robot-assisted feeding can address them. Using speculative videos that show robot behaviors within a social dining context, we interviewed participants to understand their preferences. Following a community-based participatory research method, we worked with a community researcher with motor impairments throughout this study. We contribute (a) insights into how a robot can help overcome challenges in social dining, (b) design principles for creating robot-assisted feeding systems, (c) and an implementation guide for future research in this area. Our key finding is that robots' unique assistive qualities can address challenges people with motor impairments face during social dining, promoting empowerment and belonging.

It is difficult to run long-term in-the-wild studies with mobile robots. This is partly because the robots we, as human-robot interaction (HRI) researchers, are interested in deploying prioritize expressivity over navigational capabilities, and making those robots autonomous is often not the focus of our research. One way to address these difficulties is with the Wizard of Oz (WoZ) methodology, where a researcher teleop-erates the robot during its deployment. However, the constant attention required for teleoperation limits the duration of WoZ deployments, which in-turn reduces the amount of in-the-wild data we are able to collect. Our key insight is that several types of in-the-wild mobile robot studies can be run without autonomous navigation, using wandering instead. In this paper we present and share code for our wandering robot system, which enabled Kuri, an expressive robot with limited sensor and computational capabilities, to traverse the hallways of a$28,000 \text{ ft}^{2}$floor for four days. Our system relies on informed direction selection to avoid obstacles and traverse the space, and periodic human help to charge. After presenting the outcomes from the four-day deployment, we then discuss the benefits of deploying a wandering robot, explore the types of in-the-wild studies that can be run with wandering robots, and share pointers for enabling other robots to wander. Our goal is to add wandering to the toolbox of navigation approaches HRI researchers use, particularly to run in-the-wild deployments with mobile robots.

We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual intervention. In this paper, we present a data-driven pipeline that: (1) takes in low-level data of a human shopkeeper interacting with multiple customers (28 hours of collected data); (2) autonomously extracts high-level actions from that data; and (3) learns -- without manual intervention -- how a robotic shopkeeper should respond to customers' actions online. Our proposed system for learning the interaction logic uses neural networks to first learn which customer actions are important to respond to and then learn how the shopkeeper should respond to those important customer actions. We present a novel technique for learning which customer actions are important by first learning the hidden causal relationship between customer and shopkeeper actions. In an offline evaluation, we show that our proposed technique significantly outperforms state-of-the-art baselines, in both which customer actions are important and how to respond to them.

As computing technologies continue to have a greater impact on daily life, it becomes increasingly important for the K-12 education system to prepare students for the computerized world. In this paper, we present the curriculum design, implementation, and results from a one-trimester introductory Python course that is mandatory for all 8th graders in our school district. This course is a crucial component of the K-12 computational thinking pathways we are developing at our school district, which take students from block-based programming and computational thinking (elementary school) to text-based programming and applications of computer science (high school). Our mandatory 8th grade course serves as a bridge between these two components. We present qualitative results that highlight the challenges that arose from teaching a course for all students -- not just those with a prior interest in computing -- and how the instructor overcame those challenges. We also present quantitative results that demonstrate the course's positive impact on students' attitudes towards computer science, their intent to re-engage with computer science in the future, and the gender gap with regards to confidence in computer science.

Many robot applications being explored involve robots leading humans during navigation. Developing effective robots for this task requires a way for robots to understand and model a human's following behavior. In this paper, we present results from a user study of how humans follow a guide robot in the halls of an office building. We then present a data-driven Markovian model of this following behavior, and demonstrate its generalizability across time interval and trajectory length. Finally, we integrate the model into a global planner and run a simulation experiment to investigate the benefits of coupled human-robot planning. Our results suggest that the proposed model effectively predicts how humans follow a robot, and that the coupled planner, while taking longer, leads the human significantly closer to the target position.

The ability to communicate is crucial to leading an independent life. Unfortunately, individuals from developing communities who are deaf and hard of hearing tend to encounter difficulty communicating, due to a lack of educational resources. We present findings from a two-year deployment of Speak Up, a suite of voice-powered games to motivate speech therapy, at a school for the deaf in India. Using ethnographic methods, we investigated the interplay between Speak Up and local educational practices. We found that teachers' speech therapy goals had evolved to differ from those encoded in the games, that the games influenced classroom dynamics, and that teachers had improved their computer literacy and developed creative uses for the games. We used these insights to further enhance Speak Up by creating an explicit teacher role in the games, making changes that encouraged teachers to build their computer literacy, and adding an embodied agent.