Autonomous robotic valve turning: A hierarchical learning approach

Autonomous valve turning is an extremely challenging task for an Autonomous Underwater Vehicle (AUV). To resolve this challenge, this paper proposes a set of different computational techniques integrated in a three-layer hierarchical scheme. Each layer realizes specific subtasks to improve the persistent autonomy of the system. In the first layer, the robot acquires the motor skills of approaching and grasping the valve by kinesthetic teaching. A Reactive Fuzzy Decision Maker (RFDM) is devised in the second layer which reacts to the relative movement between the valve and the AUV, and alters the robot's movement accordingly. Apprenticeship learning method, implemented in the third layer, performs tuning of the RFDM based on expert knowledge. Although the long-term goal is to perform the valve turning task on a real AUV, as a first step the proposed approach is tested in a laboratory environment.

[1]  Alin Albu-Schäffer,et al.  Aus der Forschung zum Industrieprodukt: Die Entwicklung des KUKA Leichtbauroboters , 2010, Autom..

[2]  Marc Carreras,et al.  Girona 500 AUV: From Survey to Intervention , 2012, IEEE/ASME Transactions on Mechatronics.

[3]  Dirk P. Kroese,et al.  The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics) , 2004 .

[4]  Shie Mannor,et al.  A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..

[5]  Alin Albu-Schäffer,et al.  A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots , 2007, Int. J. Robotics Res..

[6]  Darwin G. Caldwell,et al.  Towards Autonomous Robotic Valve Turning , 2015 .

[7]  Lih-Yuan Deng,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning , 2006, Technometrics.

[8]  Darwin G. Caldwell,et al.  Upper-body kinesthetic teaching of a free-standing humanoid robot , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  G. Schreiber,et al.  The Fast Research Interface for the KUKA Lightweight Robot , 2022 .

[11]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[12]  Dong-Soo Kwon,et al.  Control of underwater manipulators mounted on an ROV using base force information , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  W. Wong,et al.  On ψ-Learning , 2003 .

[14]  Jun Nakanishi,et al.  Trajectory formation for imitation with nonlinear dynamical systems , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[15]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Mongi A. Abidi,et al.  Autonomous robotic inspection and manipulation using multisensor feedback , 1991, Computer.

[17]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

[18]  David A. Anisi,et al.  Real-world demonstration of sensor-based robotic automation in oil & gas facilities , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Henk Nijmeijer,et al.  Robot Programming by Demonstration , 2010, SIMPAR.

[20]  Gianni Di Pillo,et al.  A New Version of the Price's Algorithm for Global Optimization , 1997, J. Glob. Optim..