Abstract-We present a novel approach to deal with transitions while performing a sequence of dynamic tasks with a humanoid robot. The simultaneous achievement of several tasks cannot be ensured, so we use a strategy based on weights to represent their relative importance. The robot interacts with a changing environment, and the input torques are different depending on whether the robot performs tasks in a constrained state (e.g. in contact) or not. We develop a solution with smooth weights variations and transitional tasks which avoids sharp torque evolutions. In order to validate this approach, simulations are carried out on a virtual iCub robot which is assigned the realization of a complex mission involving various changing tasks.

. On-line regression algorithms for learning mechanical models of robots: a survey. Robotics and Autonomous Systems, Elsevier, 2011, 59 (12) AbstractWith the emergence of more challenging contexts for robotics, the mechanical design of robots is becoming more and more complex. Moreover, their missions often involve unforeseen physical interactions with the environment. To deal with these difficulties, endowing the controllers of the robots with the capability to learn a model of their kinematics and dynamics under changing circumstances is becoming mandatory. This emergent necessity has given rise to a significant amount of research in the Machine Learning community, generating algorithms that address more and more sophisticated on-line modeling questions. In this paper, we provide a survey of the corresponding literature with a focus on the methods rather than on the results. In particular, we provide a unified view of all recent algorithms that outlines their distinctive features and provides a framework for their combination. Finally, we give a prospective account of the evolution of the domain towards more challenging questions.