  Learning from Demonstration is increasingly used for transferring operator
manipulation skills to robots. In practice, it is important to cater for
limited data and imperfect human demonstrations, as well as underlying safety
constraints. This paper presents a constrained-space optimization and
reinforcement learning scheme for managing complex tasks. Through interactions
within the constrained space, the reinforcement learning agent is trained to
optimize the manipulation skills according to a defined reward function. After
learning, the optimal policy is derived from the well-trained reinforcement
learning agent, which is then implemented to guide the robot to conduct tasks
that are similar to the experts' demonstrations. The effectiveness of the
proposed method is verified with a robotic suturing task, demonstrating that
the learned policy outperformed the experts' demonstrations in terms of the
smoothness of the joint motion and end-effector trajectories, as well as the
overall task completion time.
