Template-based imitation learning for manipulating symmetric objects

With the rise of small batch production, the need to shorten the development cycle has become urgent. This work aims to allow people without expert knowledge to program the robot in a single demonstration to reduce development costs. The idea of this work is to record frame relations between objects during the demonstration as templates. Then the robot is programmed by the template sequence to simplify the programming process and shorten the development time. Since symmetry will cause uncertainty in pose estimation during execution, a symmetry detection module is developed to detect the rotational symmetry of the objects through inertia tensor and all the rotation matrices are acquired using group theory. After that, a template matching module is designed to measure the distance between the current pose and possible target poses to choose the proper target pose which matches the template to avoid unreachable robot poses and accelerate the executing process. Two pick-and-place tasks, a stacking test, and a sorting task are carried out to verify our system and the results show that our method is feasible and efficient.