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Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human–cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time...
Real-time estimation of human-cloth relationship is crucial for efficient learning of motor skills in robotic clothing assistance. However, cloth state estimation using a depth sensor is a challenging problem with inherent ambiguity. To address this problem, we propose the offline learning of a cloth dynamics model by incorporating reliable motion capture data and applying this model for the online...
In this study, we propose a novel method for the real-time estimation of Human-Cloth relationship, which is crucial for efficient motor skill learning in Robotic Clothing Assistance. This system relies on the use of low cost depth sensor, which provides color and depth images without requiring an elaborate setup making it suitable for real-world applications. We present an efficient algorithm to estimate...
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