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This paper presents a new strategy to control an one-legged robot aiming to reduce the energy expended by the system. To validate this algorithm, a classic method as benchmark was used. This method has been extensively validated by simulations and experimental prototypes in the literature. For simplicity reasons, the work is restricted to the two dimensional case due to simplicity reasons. This new method is compared to the classic one with respect to performance and energy expended by the system. The model consists on a springy leg, a simple body, and an actuated hinge-type hip. The new control strategy is composed of three parts, considering the hopping height, the forward speed, and the body orientation separately. The method exploits the system passive dynamics, defined as non-forced response of the system. In this case, the model is modified adding a spring to the hip. The method defines a desired leg trajectory close to the passive hip swing movement. Simulation results for both methods are analyzed and compared

This paper presents a two-stage neural system to determine the contact points between a three-® ngered gripper and an object of arbitrary shape. In the ® rst stage, a CCD camera captures the image of the object and such an image is transformed into a two-dimensional outline through a nearest neighbour algorithm. In the second phase, two neural networks, functioning in cascade, select three contact points in the outline. A competitive Hop® eld neural network de® nes an approximate polygon considering a reduced number of boundary points of the original outline. Then, a supervised neural network, either a multi-layer perceptron or a radial basis function (RB F) network, ® nd the contact points. The experiments suggest that the RB F network trained by the global ridge regression method is suitable for on-line applications and presents the best overall performance in terms of accuracy and robustness to noise. Moreover, this method is able to ® nd correctly the contact points for objects of arbitrar y shapes.

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