Progressive Automation with DMP Synchronization and Variable Stiffness Control

Progressive automation is a method that allows the operator to demonstrate a task a few times until the robot gradually learns to execute it autonomously. In this letter, we combine a novel structure of dynamic movement primitives (DMP) with variable stiffness control, to allow synchronization with the demonstrated motion during the operator's intervention. This structure enables the DMP to speed up or slow down depending on the human demonstration. In addition, we present a variable stiffness controller to change the role of the robot between the follower and the leader based on the tracking error of the robot from the proposed DMP and on the guidance forces. The proposed variable stiffness controller is proved to be passive using energy tanks. The effectiveness of the proposed method is demonstrated experimentally.

[1]  Minija Tamosiunaite,et al.  Joining Movement Sequences: Modified Dynamic Movement Primitives for Robotics Applications Exemplified on Handwriting , 2012, IEEE Transactions on Robotics.

[2]  Aude Billard,et al.  Learning motion dynamics to catch a moving object , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[3]  Oliver Kroemer,et al.  Interaction primitives for human-robot cooperation tasks , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Sandra Hirche,et al.  An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Etienne Burdet,et al.  Slaves no longer: review on role assignment for human–robot joint motor action , 2014, Adapt. Behav..

[6]  Aude Billard,et al.  Learning from Humans , 2016, Springer Handbook of Robotics, 2nd Ed..

[7]  Zoe Doulgeri,et al.  A pHRI Framework for Modifying a Robot's Kinematic Behaviour via Varying Stiffness and Dynamical System Synchronization , 2018, 2018 26th Mediterranean Conference on Control and Automation (MED).

[8]  Zoe Doulgeri,et al.  Towards Progressive Automation of Repetitive Tasks Through Physical Human-Robot Interaction , 2017, HFR.

[9]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[10]  Jun Morimoto,et al.  Learning from demonstration and adaptation of biped locomotion , 2004, Robotics Auton. Syst..

[11]  Keng Peng Tee,et al.  Continuous Role Adaptation for Human–Robot Shared Control , 2015, IEEE Transactions on Robotics.

[12]  Alberto Montebelli,et al.  Incrementally assisted kinesthetic teaching for programming by demonstration , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[13]  Affan Pervez,et al.  Novel learning from demonstration approach for repetitive teleoperation tasks , 2017, 2017 IEEE World Haptics Conference (WHC).

[14]  François Keith,et al.  Proactive behavior of a humanoid robot in a haptic transportation task with a human partner , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[15]  Aude Billard,et al.  Passive Interaction Control With Dynamical Systems , 2016, IEEE Robotics and Automation Letters.

[16]  Matteo Saveriano,et al.  Incremental kinesthetic teaching of end-effector and null-space motion primitives , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

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

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

[19]  Cristian Secchi,et al.  A tank-based approach to impedance control with variable stiffness , 2013, 2013 IEEE International Conference on Robotics and Automation.

[20]  Stefan Schaal,et al.  From dynamic movement primitives to associative skill memories , 2013, Robotics Auton. Syst..

[21]  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).

[22]  Dongheui Lee,et al.  Incremental kinesthetic teaching of motion primitives using the motion refinement tube , 2011, Auton. Robots.

[23]  Jun Morimoto,et al.  Orientation in Cartesian space dynamic movement primitives , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).