Skill-based Shared Control

Performing a number of motion patterns – referred to as skills – (e.g., wave, spiral, sweeping motions) during teleoperation is an integral part of many industrial processes such as spraying, welding, and wiping (cleaning, polishing). Maintaining these motions whilst simultaneously avoiding obstacles and traversing complex terrain requires expert operators. In this work, we propose a novel skill-based shared control framework for incorporating the notion of skill assistance to aid novice operators to sustain these motion patterns whilst adhering to environmental constraints. Our shared control method uses streaming joystick data to estimate the model parameters that provide a description of the operator’s intention. We introduce a novel parametrization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids. This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a Model Predictive Control (MPC) loop. We perform experiments on a hardware mock-up, validating the effectiveness of our method to recognize a switch of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles. See our accompanying video here: https://youtu.be/TwhsgA6fw6M.