This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
To design a stochastic simulation experiment, it is helpful to have an estimate of the simulation run lengths required to achieve desired statistical precision. Preliminary estima...
—We present new results on reconstruction of the shape and motion of an unknown object using tactile sensors without requiring objectimmobilization. Arobotmanipulatestheobjectwit...
Abstract. A needle-tissue interaction model is an essential part of every needle insertion simulator. In this paper, a new experimental method for the modeling of needle-tissue int...
Abstract. We present a model of motor learning based on a combination of Operational Space Control and Optimal Control. Anticipatory processes are used both in the learning of the ...