  Several model-based and model-free methods have been proposed for the robot
trajectory learning task. Both approaches have their benefits and drawbacks.
They can usually complement each other. Many research works are trying to
integrate some model-based and model-free methods into one algorithm and
perform well in simulators or quasi-static robot tasks. Difficulties still
exist when algorithms are used in particular trajectory learning tasks. In this
paper, we propose a robot trajectory learning framework for precise tasks with
discontinuous dynamics and high speed. The trajectories learned from the human
demonstration are optimized by DDP and PoWER successively. The framework is
tested on the Kendama manipulation task, which can also be difficult for humans
to achieve. The results show that our approach can plan the trajectories to
successfully complete the task.
