Sensorimotor Primitives for Programming Robotic Assembly Skills

Abstract

This thesis addresses the problem of sensor-based skill composition for robotic assembly tasks. Skills are robust, reactive strategies for executing recurring tasks in our domain. In everyday life, people rely extensively on skills such as walking, climbing stairs, and driving cars; proficiency in these skills enables people to develop and robustly execute high-level plans. Unlike people, robots are unskilled -- unable to perform any task without extensive and detailed instructions from a higher-level agent. Building sensor-based, reactive skills is an important step toward realizing robots as flexible, rapidly-deployable machines. Efficiently building skills requires simultaneously reducing robot programming complexity and increasing sensor integration, which are competing and contradictory goals. This thesis attacks the problem through development of sensorimotor primitives to generalize sensor integration, graphical programming environments to facilitate skill composition, and design integration for exploiting the primitive capabilities during task design. Force and vision based sensorimotor primitives are built to provide sensor integration for task domains, not task instances. A task-level graphical programming environment provides an intuitive interface for the designer/programmer to compose the skill through a simple form of virtual demonstration. Finally, design agent representations of the primitives inside the CAD environment interact with the task model and the programmer to assist in composing the skill program. Experimental results for six different assembly tasks demonstrate sensor-based skill execution and illustrate primitive reusability.