A robot learning to transport an object combining optimal control and learning from demonstrations from is a formulation a mixture Watch Video
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Description: This video shows how a robot can learn collaborative behaviors from human demonstrations. The collaborative behavior considers robot motion adaptation to parameters of a task, extraction of the desired robot state, and variable impedance control for human-safe interaction. The human demonstrations are probabilistically encoded by a task-parametrized formulation of a Gaussian mixture model. Such encoding is later used for specifying both the desired state of the robot, and an optimal feedback con