Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of exis...
Abstract— We present an adaptive control approach combining forward kinematics model learning methods with the operational space control approach. This combination endows the rob...
Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
Abstract. We describe an approach for learning the model of the opponent in spatio-temporal negotiation. We use the Children in the Rectangular Forest canonical problem as an examp...
Abstract. In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a vari...