Abstract— We present an adaptive control approach combining forward kinematics model learning methods with the operational space control approach. This combination endows the rob...
Predictive user models often require a phase of effortful supervised training where cases are tagged with labels that represent the status of unobservable variables. We formulate a...
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 ...
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
To reduce the complexity of studying a parallel mechanism for natural language learning and understanding which supports both utterance and discourse processing, we propose a comp...