This chapter focuses on endowing robustness in the task execution step using vision‐based robot control. It presents a framework that performs all the steps of the learning process in the image space of a vision sensor. The developed approach for robot programming by demonstration (PbD) is based on the following postulations: perception of the demonstrations is performed with vision cameras; and execution of the learned strategies is conducted using visual feedback from the scene. Different camera configurations for observation of demonstrations have been used in the literature, for example, stereo pairs and multiple cameras. For generating the reference trajectories, instead of Kalman smoothing, other machine learning methods can be employed, for example, hidden Markov model (HMM), Gaussian mixture model (GMM)/Gaussian mixture regression (GMR), and dynamical systems approach. However, since the reference trajectories are subjected to correction for visual tracking, simple averaging via Kalman smoothing is adopted.


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