DocumentCode :
2693523
Title :
How-models of human reaching movements in the context of everyday manipulation activities
Author :
Nyga, Daniel ; Tenorth, Moritz ; Beetz, Michael
fYear :
2011
fDate :
9-13 May 2011
Firstpage :
6221
Lastpage :
6226
Abstract :
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.
Keywords :
human-robot interaction; learning (artificial intelligence); legged locomotion; manipulators; motion control; path planning; position control; everyday manipulation activities; human motions; human reaching movements; human-robot interaction; learning models; prediction models; semantic context; system clusters trajectories; Irrigation; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2011 IEEE International Conference on
Conference_Location :
Shanghai
ISSN :
1050-4729
Print_ISBN :
978-1-61284-386-5
Type :
conf
DOI :
10.1109/ICRA.2011.5979933
Filename :
5979933
Link To Document :
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