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.