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- W2941394317 abstract "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." @default.
- W2941394317 created "2019-05-03" @default.
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- W2941394317 date "2019-04-26" @default.
- W2941394317 modified "2023-09-30" @default.
- W2941394317 title "Data-efficient learning of robotic clothing assistance using Bayesian Gaussian process latent variable model" @default.
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- W2941394317 doi "https://doi.org/10.1080/01691864.2019.1610061" @default.
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