@article{oai:kyutech.repo.nii.ac.jp:00006517,
 author = {Koganti,  Nishanth and Shibata, Tomohiro and 柴田, 智広 and Tamei,  Tomoya and Ikeda,  Kazushi},
 issue = {15-16},
 journal = {Advanced Robotics},
 month = {Apr},
 note = {Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks.},
 pages = {800--814},
 title = {Data-efficient Learning of Robotic Clothing Assistance using Bayesian Gaussian Process Latent Variable Models},
 volume = {33},
 year = {2019},
 yomi = {シバタ, トモヒロ}
}