Expert instructors frequently use verbal instructions in motor learning coaching. The instructors find points to be improved in learners' forms and give advice. However, the verbal expressions strongly depend on instructors' characteristics, and the process of choosing instruction is not systematic in many fields. This study aims to develop an agent that can automatically generate proper verbal instructions from learners' motion data, and to systematize the instructions given by various expressions. We collected shooting motion data of free-throws in basketball from novice players, and verbal instructions to the novices' shooting motions from experienced players. Using multi-label classification, a type of supervised learning, we developed an agent that generates essential instructions to the novices' motions. We also suggested a possibility to extract semantic relationships among instructions with various expressions in the context of motor learning by using network structure inference in graph theory.

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 estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model.

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 tracking of human-cloth relationship using a depth sensor. In this study, we evaluate the performance of using a shared Gaussian Process Latent Variable Model in learning the dynamics of clothing articles. The experimental results demonstrate the effectiveness of shared GP-LVM in capturing cloth dynamics using few data samples and the ability to generalize to unseen settings. We further demonstrate three key factors that affect the predictive performance of the trained dynamics model.

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 the parameters that represent the topological relationship between human and the clothing article. At the core of our approach are low dimensional representation of Human-Cloth relationship using topology coordinates for fast learning of motor skills and a unified ellipse fitting algorithm for the compact representation of the state of clothing articles. We conducted experiments that illustrate the robustness of these feature representations. Furthermore, we evaluated the performance of our proposed method by applying it to real-time clothing assistance tasks and compared the estimates provided by our method with the ground truth.