Movement primitives via optimization

We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot's ability to accurately generalize the demonstration.

[1]  Jan Peters,et al.  Reinforcement Learning to Adjust Robot Movements to New Situations , 2010, IJCAI.

[2]  Stefan Schaal,et al.  STOMP: Stochastic trajectory optimization for motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[3]  Ross A. Knepper,et al.  Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home , 2012, Proceedings of the IEEE.

[4]  Chonhyon Park,et al.  ITOMP: Incremental Trajectory Optimization for Real-Time Replanning in Dynamic Environments , 2012, ICAPS.

[5]  Pieter Abbeel,et al.  Learning from Demonstrations Through the Use of Non-rigid Registration , 2013, ISRR.

[6]  Jan Peters,et al.  Learning motor primitives for robotics , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[8]  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).

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

[10]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[11]  Marc Toussaint,et al.  Trajectory prediction: learning to map situations to robot trajectories , 2009, ICML '09.

[12]  Darwin G. Caldwell,et al.  Robot motor skill coordination with EM-based Reinforcement Learning , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[14]  Stefan Schaal,et al.  Skill learning and task outcome prediction for manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[16]  Satoshi Endo,et al.  Dynamic Movement Primitives for Human-Robot interaction: Comparison with human behavioral observation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  J. Andrew Bagnell,et al.  Maximum margin planning , 2006, ICML.

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

[19]  Ron Alterovitz,et al.  Demonstration-Guided Motion Planning , 2011, ISRR.

[20]  Christopher G. Atkeson,et al.  Policies based on trajectory libraries , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[21]  Jun Nakanishi,et al.  Learning Attractor Landscapes for Learning Motor Primitives , 2002, NIPS.