A robot learning to transport an object
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 control law that exploits the variability in position, velocity and force spaces observed during the demonstrations. The whole framework allows the robot to modify its movements as a function of parameters of the task, while showing different impedance behaviors.