Bilateral physical interaction with a robot manipulator through a weighted combination of flow fields

When collaboration between human users and robots involves physical interaction, the importance of the safety issue arises. We propose a method to transfer to robots several tasks demonstrated by the user through kinesthetic teaching and subsequently learned using a weighted combination of dynamical systems (DS). The approach used to encode the desired skills ensures a safe robot behavior during the task reproduction, allowing physical interaction with the user who can employ the manipulator as a tangible interface. By using a force sensor-less impedance controller with a back-drivable robot, this concept is exploited in two physical human-robot interaction (pHRI) scenarios. The first considers an emergency situation in which the user can stop or pause a task execution by grasping and moving the robot away from the region of space associated to the skill. The second studies the possibility to select one among several learned tasks and switch to its execution by physically guiding the robot towards the task region.

[1]  Ioannis Iossifidis,et al.  Dynamical Systems Approach for the Autonomous Avoidance of Obstacles and Joint-limits for an Redundant Robot Arm , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Aude Billard,et al.  Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Paul Evrard,et al.  Learning collaborative manipulation tasks by demonstration using a haptic interface , 2009, ICAR.

[4]  Antonio Bicchi,et al.  Integration of active and passive compliance control for safe human-robot coexistence , 2009, 2009 IEEE International Conference on Robotics and Automation.

[5]  Darwin G. Caldwell,et al.  Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[7]  Ferdinando A. Mussa-Ivaldi,et al.  From basis functions to basis fields: vector field approximation from sparse data , 1992, Biological Cybernetics.

[8]  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).

[9]  Darwin G. Caldwell,et al.  Learning and Reproduction of Gestures by Imitation , 2010, IEEE Robotics & Automation Magazine.

[10]  Stefan Schaal,et al.  Dynamics systems vs. optimal control--a unifying view. , 2007, Progress in brain research.

[11]  Ioannis Iossifidis,et al.  Generating collision free reaching movements for redundant manipulators using dynamical systems , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Dana Kulic,et al.  Pre-collision safety strategies for human-robot interaction , 2007, Auton. Robots.

[13]  Oussama Khatib,et al.  A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..

[14]  Antonio Bicchi,et al.  Fast and "soft-arm" tactics [robot arm design] , 2004, IEEE Robotics & Automation Magazine.

[15]  Stefan Schaal,et al.  Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  Darwin G. Caldwell,et al.  Encoding the time and space constraints of a task in explicit-duration Hidden Markov Model , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Tamim Asfour,et al.  Imitation Learning of Dual-Arm Manipulation Tasks in Humanoid Robots , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[18]  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).

[19]  Dongheui Lee,et al.  Incremental motion primitive learning by physical coaching using impedance control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Eric L. Sauser,et al.  An Approach Based on Hidden Markov Model and Gaussian Mixture Regression , 2010 .

[21]  Alessandro De Luca,et al.  Exploiting robot redundancy in collision detection and reaction , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Klas Kronander,et al.  Learning to control planar hitting motions in a minigolf-like task , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.