The video shows a Barrett WAM 7 DOFs manipulator learning an ironing task by imitation. The set of demonstrated movements are encoded in a mixture of basis force fields (Dynamic Movement Primitives (DMP) model extended to the use of variation and correlation information). The task redundancies are exploited during reproduction to regulate the robot’s stiffness and the dynamics of the movement.
The robot is thus compliant in parts of the task that do not require to track precisely a reference trajectory.
For the example of the ironing task, the system learns that it is more important to track the movement in the vertical direction than in the other two directions of the horizontal plane. The video then shows how to exploit this redundancy of the task to satisfy other constraints in parallel, such as moving away from the user to prevent collision.

Video credits:
Dr Sylvain Calinon
Dr Irene Sardellitti
(Italian Institute of Technology)

Link:
programming-by-demonstration.org/SylvainCalinon/