Learned Partial Automation for Shared Control in Tele-Robotic Manipulation

When used in challenging applications like surgery or underwater maintenance, the use of tele-operated robots involves manipulations that are complex to perform on the master controllers due to restricted access and limited perception. In this paper, we investigate an assistance approach for tele-robotic manipulation, in which the robot automates several degrees of freedom (DOF) of the tools, such as their orientation. This automation requires the understanding of the intent of the operator, so as to not impede the natural manipulation of the remaining DOF. Our system is therefore based on the observation that in the aforementioned applications, the manipulation tasks have often a structure that can be learned from the daily usage of the robot. We propose an approach that uses the typical motion performed by the operator during a given task, learned from demonstration, to automate the rotation of the manipulator in new instances of this task. The operator keeps control of the robot by manipulating the tool translation and can recover full control if needed. The learned motion model is based on Gaussian Mixture Regressions and combined with a 3D reconstruction of the environment to address variations in the task. We demonstrate our assistance approach using a da Vinci robot on a task consisting of moving a ring along a wire possessing a complex 3D shape.