Bayesian Nonparametric Learning of Cloth Models for Real-Time State Estimation
Bayesian Nonparametric Learning of Cloth Models for Real-Time State Estimation
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DOI：
10.1109/tro.2017.2691721
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发表时间：
2017-05
影响因子：
7.8
通讯作者：
Nishanth Koganti;Tomoya Tamei;K. Ikeda;T. Shibata
中科院分区：
文献类型：
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作者：
Nishanth Koganti;Tomoya Tamei;K. Ikeda;T. Shibata
衣物辅助机器人解决方案可以显著改善老年人和残疾人的生活质量。实时估计的人布关系是至关重要的有效学习的机器人服装援助的运动技能。所涉及的主要挑战是由于固有的非刚性和闭塞的布状态估计。在这项研究中，我们提出了一种新的框架，使用低成本的深度传感器的实时估计的布状态，使其适合于一个可行的社会实现。该框架依赖于假设，服装制品被约束到一个低维的潜在流形在服装任务。我们建议使用流形相关性确定（MRD）来学习离线布料模型，该模型可用于在真实的时间中执行知情的布料状态估计。使用来自运动捕捉系统和深度传感器的观察来训练布料模型。MRD提供了一种原则性的概率框架，用于在只有噪声深度传感器特征状态真实的可用时推断准确的运动捕捉状态。实验结果表明，我们的框架能够使用很少的数据样本学习一致的特定于任务的潜在特征，并有能力推广到看不见的环境设置。我们进一步提出了几个因素，影响学习的衣服状态模型的预测性能。
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.