How-models of human reaching movements in the context of everyday manipulation activities
How-models of human reaching movements in the context of everyday manipulation activities
复制标题
日常操纵活动背景下人类伸手运动的方法模型
DOI：
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复制
发表时间：
2011
期刊：
影响因子：
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通讯作者：
M. Beetz
中科院分区：
文献类型：
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作者：
D. Nyga;Moritz Tenorth;M. Beetz
我们提出了一个在日常操作活动中学习人类到达轨迹的模型的系统。不同类型的轨迹被自动发现，每一种轨迹都由其语义上下文来描述。在第一步中，该系统根据轨迹的形状对人类日常活动的观察中的轨迹进行分类，然后学习这些轨迹与使用它们的上下文之间的关系。所得到的模型可用于机器人选择要在给定环境中使用的轨迹。它们还可以作为强大的人体运动预测模型，以改善人与机器人的交互。在TUM厨房数据集上的实验表明，该方法能够在摆桌子等日常活动的真实世界观察中发现有意义的簇。
We present a system for learning models of human reaching trajectories in the context of everyday manipulation activities. Different kinds of trajectories are automatically discovered, and each of them is described by its semantic context. In a first step, the system clusters trajectories in observations of human everyday activities based on their shapes, and then learns the relation between these trajectories and the contexts in which they are used. The resulting models can be used for robots to select a trajectory to use in a given context. They can also serve as powerful prediction models for human motions to improve human-robot interaction. Experiments on the TUM kitchen data set show that the method is capable of discovering meaningful clusters in real-world observations of everyday activities like setting a table.