On the Integration of Hardware-Abstracted Robot Skills for use in Industrial Scenarios

In this paper, we present a method for programming robust, reusable and hardware-abstracted robot skills. The goal of this work is to supply mobile robot manipulators with a library of skills that incorporate both sensing and action, which permit robot novices to easily reprogram the robots to perform new tasks, as well as provide a more clear mapping between human and robot actions. Critical to the success of this approach is the notion of hardware abstraction, that separates the skill level from the primitive level on specific systems. Leveraging a previously proposed architecture, we construct two complex skills by instantiating the necessary skill primitives on two very different mobile manipulators. The skills are parameterized by task level variables, such as object labels and environment locations, making re-tasking the skills by operators feasible.

[1]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[2]  C. Breazeal,et al.  Robots that imitate humans , 2002, Trends in Cognitive Sciences.

[3]  Matthew T. Mason,et al.  Compliance and Force Control for Computer Controlled Manipulators , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Adrian Hilton,et al.  Visual Analysis of Humans - Looking at People , 2013 .

[5]  Joris De Schutter,et al.  Specification of force-controlled actions in the "task frame formalism"-a synthesis , 1996, IEEE Trans. Robotics Autom..

[6]  Ole Madsen,et al.  Does your Robot have Skills , 2012 .

[7]  Jean-Claude Latombe,et al.  A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking , 2001, ISRR.

[8]  Ole Madsen,et al.  Identifying and evaluating suitable tasks for autonomous industrial mobile manipulators (AIMM) , 2012 .

[9]  Christopher W. Geib,et al.  Title of the Deliverable: Publication about Multi-level Learning Sys- Tem Attachment 1 Attachment 2 a Formal Definition of Object-action Complexes and Examples at Different Levels of the Processing Hierarchy , 2022 .

[10]  G. Rizzolatti,et al.  Premotor cortex and the recognition of motor actions. , 1996, Brain research. Cognitive brain research.

[11]  A F Bobick,et al.  Movement, activity and action: the role of knowledge in the perception of motion. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[12]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[13]  José Santos-Victor,et al.  A Developmental Roadmap for Learning by Imitation in Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Mikkel Rath Pedersen,et al.  Using human gestures and generic skills to instruct a mobile robot arm in a feeder filling scenario , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[15]  Rüdiger Dillmann,et al.  Teaching and learning of robot tasks via observation of human performance , 2004, Robotics Auton. Syst..

[16]  J. De Schutter,et al.  Compliant Robot Motion I. A Formalism for Specifying Compliant Motion Tasks , 1988 .

[17]  Lydia E. Kavraki,et al.  The Open Motion Planning Library , 2012, IEEE Robotics & Automation Magazine.

[18]  Friedrich M. Wahl,et al.  Executing assembly tasks specified by manipulation primitive nets , 2005, Adv. Robotics.

[19]  Sven Molkenstruck,et al.  A manipulator plays Jenga , 2008, IEEE Robotics & Automation Magazine.

[20]  Anders Robertsson,et al.  On the integration of skilled robot motions for productivity in manufacturing , 2011, 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM).

[21]  Christopher W. Geib,et al.  The meaning of action: a review on action recognition and mapping , 2007, Adv. Robotics.

[22]  Friedrich M. Wahl,et al.  Manipulation Primitives - A Universal Interface between Sensor-Based Motion Control and Robot Programming , 2011, Robotic Systems for Handling and Assembly.