Ariel Kapusta
Papers
17
Total Citations
404
H-Index
9
About
Ariel Kapusta is a robotics researcher whose work centers on assistive robotics, human-robot interaction, and robot-assisted activities of daily living (ADLs). His research has made significant contributions to helping people with disabilities perform fundamental tasks such as dressing and feeding through intelligent robotic systems. Kapusta is perhaps best known for his pioneering work in robot-assisted dressing, where he developed TOORAD (Task Optimization of Robot-Assisted Dressing), a framework for generating collaborative human-robot action plans through optimization and simulation (62 citations). Complementing this, he advanced haptic perception and capacitive proximity sensing to enable robots to track human pose and respond to physical interactions during dressing (48 and 45 citations respectively). His haptic simulation environment for dressing further reduced the need for costly real-world training trials (38 citations). Beyond dressing, Kapusta developed a multimodal anomaly detection system for robot-assisted feeding (60 citations) and explored model predictive control for robot reaching in cluttered environments (45 citations). His broader vision includes integrated bedside assistance systems combining robotic beds with mobile manipulators. Across his career, Kapusta's research has consistently prioritized practical, human-centered robotic solutions that meaningfully improve quality of life for individuals with physical impairments.
Research Focus
Key Achievements
Top Papers
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- 4Model predictive control for fast reaching in clutter45 citations · 2015
- 5Data-driven haptic perception for robot-assisted dressing45 citations · 2016
- 6Haptic simulation for robot-assisted dressing38 citations · 2017
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