— Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to ...
We present an approach that uses Q-learning on individual robotic agents, for coordinating a missiontasked team of robots in a complex scenario. To reduce the size of the state sp...
This paper discusses how a robot can develop its state vector according to the complexity of the interactions with its environment. A method for controlling the complexity is prop...
Robotic controllers take advantage from neural network learning capabilities as long as the dimensionality of the problem is kept moderate. This paper explores the possibilities of...
We present an artificial neural network used to learn online complex temporal sequences of gestures to a robot. The system is based on a simple temporal sequences learning architec...