Search for: All records

Over 1 billion people worldwide are estimated to experience significant disability, which impacts their ability to independently conduct activities of daily living (ADLs) such as eating, ambulating, and dressing. Physically assistive robots (PARs) have emerged as a promising technology to help people with disabilities conduct ADLs, thereby restoring independence and reducing caregiver burden. However, despite decades of research on PARs, deployments of them in end-users’ homes are still few and far between. This thesis focuses on robot-assisted feeding as a case study for how we can achieve in-home deployments of PARs. Our ultimate goal is to develop a robot-assisted feeding system that enables any user, in any environment, to feed themselves a meal of their choice in a way that aligns with their preferences. We collaborate closely with 2 community researchers with motor impairments to design, implement, and evaluate a robot-assisted feeding system that makes progress towards this ultimate goal. Specifically, this thesis presents the following work: 1. A systematic survey of research on PARs, identifying key themes and trends; 2. A formative study investigating the meal-related needs of people with motor impairments and their priorities regarding the design of robot-assisted feeding systems; 3. An action schema and unsupervised learning pipeline that uses human data to learn representative actions a robot can use acquire diverse bites of food; and 4. The key system design considerations, both software and hardware, that enabled us to develop a robot-assisted feeding system to deploy in users’ homes. We evaluate the system with two studies: (1) an out-of-lab study where 5 participants and 1 community researcher use the robot to feed themselves a meal of their choice in a cafeteria, conference room, or office; and (2) a 5-day, in-home deployment where 1 community researcher uses the robot to feed himself 10 meals across various spatial, social, and activity contexts. The studies reveal promising results in terms of the usability and functionality of the system, as well as key directions for future work that are necessary to achieve the aforementioned ultimate goal. We present key lessons learned regarding in-home deployments of PARs: (1) spatial contexts are numerous, customizability lets users adapt to them; (2) off-nominals will arise, variable autonomy lets users overcome them; (3) assistive robots’ benefits depend on context; and (4) work with end-users and stakeholders. 

More than 1 billion people in the world are estimated to experience significant disability. These disabilities can impact people's ability to independently conduct activities of daily living, including ambulating, eating, dressing, taking care of personal hygiene, and more. Mobile and manipulator robots, which can move about human environments and physically interact with objects and people, have the potential to assist people with disabilities in activities of daily living. Although the vision of physically assistive robots has motivated research across subfields of robotics for decades, such robots have only recently become feasible in terms of capabilities, safety, and price. More and more research involves end-to-end robotic systems that interact with people with disabilities in real-world settings. In this article, we survey papers about physically assistive robots intended for people with disabilities from top conferences and journals in robotics, human–computer interactions, and accessible technology, to identify the general trends and research methodologies. We then dive into three specific research themes—interaction interfaces, levels of autonomy, and adaptation—and present frameworks for how these themes manifest across physically assistive robot research. We conclude with directions for future research. 

Backchanneling behaviors on a robot, such as nodding, can make talking to a robot feel more natural and engaging by giving a sense that the robot is actively listening. For backchanneling to be effective, it is important that the timing of such cues is appropriate given the humans’ conversational behaviors. Recent progress has shown that these behaviors can be learned from datasets of human-human conversations. However, recent data-driven methods tend to overfit to the human speakers that are seen in training data and fail to generalize well to previously unseen speakers. In this paper, we explore the use of data augmentation for effective nodding behavior in a robot. We show that, by augmenting the input speech and visual features, we can produce data-driven models that are more robust to unseen features without collecting additional data. We analyze the efficacy of data-driven backchanneling in a realistic human-robot conversational setting with a user study, showing that users perceived the data-driven model to be better at listening as compared to rule-based and random baselines. 

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.