Bruno D. Damas

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— In this paper a novel approach to kinematics learning and task space control, under switching contexts, is presented. Such non-stationary contexts may appear in many robotic tasks: in particular, the changing of the context due to the use of tools with different lengths and shapes is herein studied. We model the robot forward kinematics as a multi-valued(More)
— This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these models online, while executing a desired task. Specifically, we employ a recent learning algorithm(More)
We present a supervised learning algorithm for estimation of generic input-output relations in a real-time, online fashion. The proposed method is based on a generalized expectation-maximization approach to fit an infinite mixture of linear experts (IMLE) to an online stream of data samples. This probabilistic model, while not fully Bayesian, can(More)
— Accurate dynamic models can be very difficult to compute analytically for complex robots; moreover, using a pre-computed fixed model does not allow to cope with unexpected changes in the system. An interesting alternative solution is to learn such models from data, and keep them up-to-date through online adaptation. In this paper we consider the problem(More)