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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.
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Physically Assistive Robots: A Systematic Review of Mobile and Manipulator Robots That Physically Assist People with Disabilities
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.
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- Award ID(s):
- 1924435
- PAR ID:
- 10492877
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Control, Robotics, and Autonomous Systems
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2573-5144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-control-062823-024352
