Data-efficient learning of robotic clothing assistance using Bayesian Gaussian process latent variable model

• Published in: Advanced robotics 2019-08, Vol.33 (15-16), p.800-814
• Main Authors: Koganti, Nishanth, Shibata, Tomohiro, Tamei, Tomoya, Ikeda, Kazushi
• Format: Article
• Language: English
• Subjects: Clothing assistance; Gaussian processes; latent variable models; learning from demonstration; policy search reinforcement learning
• ISSN: 0169-1864; 1568-5535
• DOI: 10.1080/01691864.2019.1610061

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.