Bruno D. Damas

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We present a new approach to cope with unknown redundant systems. For this we present i) an online algorithm that learns general input-output restrictions and, ii) a method that, given a partial set of input-output variables, provides an estimate of the remaining ones, using the learned restrictions. We show applications of the algorithm using examples of(More)
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)
Robotic obstacle avoidance in cluttered and dense environments is an important issue in robotic navigation. Over the past few years a number of techniques has been proposed to deal with safe navigation among obstacles in unknown scenarios. Unfortunately many of these methods do not consider obstacle velocities, which can rise some serious questions(More)
This paper introduces a method to model multi-robot teams using stochastic discrete event system techniques. The environment state space and robot behaviours are discretised and modelled by modular finite state automata (FSA). Then, all the FSA are composed to obtain the complete model of the team situated in its environment. Controllable and uncontrollable(More)
This paper proposes a supervised algorithm for online learning of input-output relations that is particularly suitable to simultaneously learn the forward and inverse kinematics of general manipulators - the multi-valued nature of the inverse kinematics of serial chains and forward kinematics of parallel manipulators makes it infeasible to apply(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)