Abstract: A computer-implemented method includes recording, with a three-dimensional camera, one or more demonstrations of a user performing one or more reaching tasks. Training data is computed to describe the one or more demonstrations. One or more weights of a neural network are learned based on the training data, where the neural network is configured to estimate a goal location of the one or more reaching tasks. A partial trajectory of a new reaching task is recorded. An estimated goal location is computed, by a computer processor, by applying the neural network to the partial trajectory of the new reaching task.

Abstract: A computer-implemented method includes recording one or more demonstrations of a task performed by a user. Movements of one or more joints of the user are determined from the one or more demonstrations. By a computer processor, a neural network or Gaussian mixture model incorporating one or more contraction analysis constraints is learned, based on the movements of the one or more joints of the user, the one or more contraction analysis constraints representing motion characteristics of the task. A first initial position of a robot is determined. A first trajectory of the robot is determined to perform the task, based at least in part on the neural network or Gaussian mixture model and the first initial position.

Abstract: A computer-implemented method includes recording, with a three-dimensional camera, one or more demonstrations of a user performing one or more reaching tasks. Training data is computed to describe the one or more demonstrations. One or more weights of a neural network are learned based on the training data, where the neural network is configured to estimate a goal location of the one or more reaching tasks. A partial trajectory of a new reaching task is recorded. An estimated goal location is computed, by a computer processor, by applying the neural network to the partial trajectory of the new reaching task.

Abstract: A computer-implemented method includes recording one or more demonstrations of a task performed by a user. Movements of one or more joints of the user are determined from the one or more demonstrations. By a computer processor, a neural network or Gaussian mixture model incorporating one or more contraction analysis constraints is learned, based on the movements of the one or more joints of the user, the one or more contraction analysis constraints representing motion characteristics of the task. A first initial position of a robot is determined. A first trajectory of the robot is determined to perform the task, based at least in part on the neural network or Gaussian mixture model and the first initial position.

Abstract: A method apparatus estimates depths of features observed in a sequence of images acquired of a scene by a moving camera by first locating features, estimating coordinates of the features and generating a sequence of perspective feature image. A set of differential equations are applied to the sequence of perspective feature images to form a nonlinear dynamic state estimator for the depths using only a vector of linear and angular velocities of the camera and the focal length of the camera. The camera can be mounted on a robot manipulator end effector. The velocity of the camera is determined by robot joint encoder measurements and known robot kinematics. An acceleration of the camera is obtained by differentiating the velocity and the acceleration is combined with other signals.