A Passivity-Based Strategy for Coaching in Human-Robot Interaction

In order to make robot programming more easy and immediate, walk-through programming techniques can be exploited. However, a modification of a portion of the trajectory usually means to execute the path from the beginning. In this paper we propose a passivity-based framework to modify the trajectory online, manually driving the robot throughout the desired correction. The system follows the initial trajectory, encoded with Dynamical Movement Primitives, by setting high gains in the admittance control. When the human operator grabs the end-effector, the robot becomes compliant and the user can easily teach the desired correction, until he/she releases it at the end of the modification. Finally, the correction is optimally joined to the initial trajectory, restarting the path tracking. To avoid unsafe behaviors, the variation of the admittance parameters is performed exploiting energy tanks, in order to preserve the passivity of the interaction.

[1]  Aude Billard,et al.  Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models , 2011, IEEE Transactions on Robotics.

[2]  Dongjun Lee,et al.  Passive-Set-Position-Modulation Framework for Interactive Robotic Systems , 2010, IEEE Transactions on Robotics.

[3]  Saverio Farsoni,et al.  Compensation of Load Dynamics for Admittance Controlled Interactive Industrial Robots Using a Quaternion-Based Kalman Filter , 2017, IEEE Robotics and Automation Letters.

[4]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[5]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[6]  Andrej Gams,et al.  On-line coaching of robots through visual and physical interaction: Analysis of effectiveness of human-robot interaction strategies , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Abderrahmane Kheddar,et al.  Motion learning and adaptive impedance for robot control during physical interaction with humans , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  Jimmy A. Jørgensen,et al.  Adaptation of manipulation skills in physical contact with the environment to reference force profiles , 2015, Auton. Robots.

[9]  Luigi Villani,et al.  Force Control , 2016, Springer Handbook of Robotics, 2nd Ed..

[10]  Lorenzo Sabattini,et al.  Admittance control parameter adaptation for physical human-robot interaction , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Martin Buss,et al.  Force Tracking Impedance Control with Variable Target Stiffness , 2008 .

[12]  Sami Haddadin,et al.  Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Stefan Schaal,et al.  Online movement adaptation based on previous sensor experiences , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  Carme Torras,et al.  A robot learning from demonstration framework to perform force-based manipulation tasks , 2013, Intelligent Service Robotics.

[16]  Stefano Stramigioli,et al.  Position Drift Compensation in Port-Hamiltonian Based Telemanipulation , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Alberto Montebelli,et al.  Learning in-contact control strategies from demonstration , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Riccardo Muradore,et al.  An Energy Tank-Based Interactive Control Architecture for Autonomous and Teleoperated Robotic Surgery , 2015, IEEE Transactions on Robotics.

[19]  Alexander Dietrich,et al.  Passivation of Projection-Based Null Space Compliance Control Via Energy Tanks , 2016, IEEE Robotics and Automation Letters.

[20]  Aude Billard,et al.  Stability Considerations for Variable Impedance Control , 2016, IEEE Transactions on Robotics.

[21]  Anders Robertsson,et al.  Autonomous interpretation of demonstrations for modification of dynamical movement primitives , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Andrej Gams,et al.  Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks , 2014, IEEE Transactions on Robotics.

[23]  Carme Torras,et al.  Robot learning from demonstration of force-based tasks with multiple solution trajectories , 2011, 2011 15th International Conference on Advanced Robotics (ICAR).

[24]  Alin Albu-Schäffer,et al.  Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions , 2011, IEEE Transactions on Robotics.

[25]  Stefano Stramigioli,et al.  Bilateral Telemanipulation With Time Delays: A Two-Layer Approach Combining Passivity and Transparency , 2011, IEEE Transactions on Robotics.

[26]  Stefan Schaal,et al.  Variable Impedance Control - A Reinforcement Learning Approach , 2010, Robotics: Science and Systems.

[27]  Cristian Secchi,et al.  Tool compensation in walk-through programming for admittance-controlled robots , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[28]  Andrej Gams,et al.  Bimanual human robot cooperation with adaptive stiffness control , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).