To be effective, action learning should not be limited to direct replication of movements obtained during training but must also enable the generation of actions in situations a robot has never encountered before. This paper describes a methodology that enables the generalization of the available sensorimotor knowledge using statistical techniques.
COBISS.SI-ID: 23918375
In this article, we presented a compete concept of learning action primitives to recognize and synthesize actions. One of the main novelties in this work is the detection of primitives in a unified framework, which takes into account objects and actions being applied to them. We proposed an unsupervised learning approach for action primitives that automatically identifies the observed primitives and the associated action grammar.
COBISS.SI-ID: 23725095