A Learning from demonstration approach for robot trajectories through motion-sensing human demonstrations

The objective of this thesis is to teach a Baxter robot to learn certain arm trajectories. The robot must be capable of generalizing the primitive movement of the trajectory to new unseen poses. The thesis is framed within a robotized kitchen project with aims to help people with mobility problems. To solve this problem end, a human will record demonstrations, which will be translated to the robots’ morphology using an Inverse Kinematics (IK) module. For the learning part Dynamic Movement Primitives (DMP) will be used, due to their capability to take profit of human experience. The proposed system works in the majority of the scenarios, but, it would be expected to behave better when generalizing to new orientations of the arm. However a proposal has been suggested to correct this issue