We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER takes as inpu...
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI exe...
: A system for learning the pre-grasp positioning task for a robot manipulator is presented. The images delivered from a gripper mounted camera are analysed using Gabor filters wh...
For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of label...
Zhe Li, Sven Wachsmuth, Jannik Fritsch, Gerhard Sa...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...