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In daily life, most hand movements involve the simultaneous activation of multiple fingers. Models generated by semiunsupervised learning in which only individual finger activation data are used in training have recently been suggested for simultaneous and proportional control of prosthetic robot hands. Although training with many datasets should be avoided, simultaneous activation data need to be...
Generating robotic grasps for given tasks is a difficult problem. This paper proposes a learning-based approach to generate suitable partial power grasp for a set of tool-using tasks. First a number of valid partial power grasps are sampled in simulation and encoded as a probabilistic model, which encapsulates the relations among the task-specific contact, the graspable object feature and the finger...
The interaction with robotic devices by means of physiological human signals has become of great interest in the last years because of the capability of catching human intention of movement and translate it in a coherent action performed by a robotic platform. Due to the complexity of EMG signals, several studies have been carried out about models built on a single subject (subject-specific). However,...
Robot calibration is to improve the accuracy of the robot model so as to achieve better positioning accuracy within the robot work cell. Model based calibration approaches are in general limited to compensating for geometric errors and are unable to compensate for error sources that do not fit within the proposed robot model. In order to compensate for the unmodeled error sources, a Radial Basis Function...
In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands...
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