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In robotics motion is often described from an external perspective, i.e., wegive information on the obstacle motion in a mathematical manner with respectto a specific (often inertial) reference frame. In the current work, we proposeto describe the robotic motion with respect to the robot itself. Similar to howwe give instructions to each other (go straight, and then after multiple metersmove left, and then a sharp turn right.), we give the instructions to a robotas a relative rotation. We first introduce an obstacle avoidance framework thatallows avoiding star-shaped obstacles while trying to stay close to an initial(linear or nonlinear) dynamical system. The framework of the local rotation isextended to motion learning. Automated clustering defines regions of localstability, for which the precise dynamics are individually learned. Theframework has been applied to the LASA-handwriting dataset and shows promisingresults.

From Obstacle Avoidance To Motion Learning Using Local Rotation of Dynamical Systems