A Robot-Agnostic Framework to Learn Position-Force Controlled Robotic Applications

Human-to-robot knowledge transfer plays a pivotal role in letting robotics adapt to a wide range of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tasks</i>. This transfer should provide the robot with a set of building blocks representing both the semantic knowledge of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task</i> and the sequence of position/force primitives to achieve it. Furthermore, aiming to provide the robot with autonomous decision making and handling of unstructured workspaces, it should schedule the best course of action, even in case of unexpected events. This work proposes a framework that exploits Programming by Demonstration (PbD) granting human to robot <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Task</i> knowledge transfer, resulting in semantic Behaviour Trees (BTs). These encapsulate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Skills</i>’ semantics through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predicates</i> and their execution through position and force primitives. Two experiments and an industrial-like use case show how the framework, together with a knowledge base and a PDDL planner, provides different industrial robot brands autonomy in decision making, flexibility to different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tasks</i> and reactiveness to unexpected events.