How-Models of Human Reaching Movements in the Context of Everyday Manipulation Activities

摘要：

We present a system for learning models of human reaching trajectories in the context of everyday manipulation activities. Different kinds of trajectories are automatically dis covered, 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.

作者: Daniel Nyga Moritz Tenorth Michael Beetz

作者单位: Intelligent Autonomous Systems,Technische Universit(a)t M(u)nchen

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 6221-6226

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）