A robotic shared control teleoperation method based on learning from demonstrations

In teleoperation, the operator is often required to command the motion of the remote robot and monitor its behavior. However, such an interaction demands a heavy workload from a human operator when facing with complex tasks and dynamic environments. In this article, we propose a shared control method to assist the operator in the manipulation tasks to reduce the workload and improve the efficiency. We adopt a task-parameterized hidden semi-Markov model to learn a manipulation skill from several human demonstrations. We utilize the learned model to predict the manipulation target given the current observed robotic motion trajectory and subsequently estimate the desired robotic motion given the current input of the operator. The estimated robotic motion is then utilized to correct the input of the operator to provide manipulation assistance. In addition, a set of virtual reality devices are used to capture the operator's motion and display the vision feedback from the remote site. We evaluate our approach through two manipulation tasks with a dual-arm robot. The experimental results show the effectiveness of the proposed method.