Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting

Modelling of contact-rich tasks is challenging and cannot be entirely solved using classical control approaches due to the difficulty of constructing an analytic description of the contact dynamics. Additionally, in a manipulation task like food-cutting, purely learning-based methods such as Reinforcement Learning, require either a vast amount of data that is expensive to collect on a real robot, or a highly realistic simulation environment, which is currently not available. This paper presents a data-driven control approach that employs a recurrent neural network to model the dynamics for a Model Predictive Controller. We build upon earlier work limited to torque-controlled robots and redefine it for velocity controlled ones. We incorporate force/torque sensor measurements, reformulate and further extend the control problem formulation. We evaluate the performance on objects used for training, as well as on unknown objects, by means of the cutting rates achieved and demonstrate that the method can efficiently treat different cases with only one dynamic model. Finally we investigate the behavior of the system during force-critical instances of cutting and illustrate its adaptive behavior in difficult cases.

[1]  John J. Craig,et al.  Hybrid position/force control of manipulators , 1981 .

[2]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[3]  Hendrik Van Brussel,et al.  Compliant Robot Motion I. A Formalism for Specifying Compliant Motion Tasks , 1988, Int. J. Robotics Res..

[4]  H. Michalska,et al.  Receding horizon control of nonlinear systems , 1988, Proceedings of the 28th IEEE Conference on Decision and Control,.

[5]  V. Mehrmann.  The Autonomous Linear Quadratic Control Problem: Theory and Numerical Solution , 1991 .

[6]  Lorenzo Sciavicco,et al.  The parallel approach to force/position control of robotic manipulators , 1993, IEEE Trans. Robotics Autom..

[7]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[8]  Zoe Doulgeri,et al.  An adaptive law for slope identification and force position regulation using motion variables , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Anil V. Rao,et al.  ( Preprint ) AAS 09-334 A SURVEY OF NUMERICAL METHODS FOR OPTIMAL CONTROL , 2009 .

[10]  Carl E. Rasmussen,et al.  PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.

[11]  P. Gierlak,et al.  Adaptive hybrid position/force control of manipulator , 2012 .

[12]  Sergey Levine,et al.  Learning Complex Neural Network Policies with Trajectory Optimization , 2014, ICML.

[13]  Sergey Levine,et al.  Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics , 2014, NIPS.

[14]  Martin A. Riedmiller,et al.  Approximate real-time optimal control based on sparse Gaussian process models , 2014, 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

[15]  Aude Billard,et al.  Learning object-level impedance control for robust grasping and dexterous manipulation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Aude Billard,et al.  Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction , 2014, IEEE Transactions on Haptics.

[17]  Masayoshi Tomizuka,et al.  A Learning-Based Framework for Robot Peg-Hole-Insertion , 2015, HRI 2015.

[18]  Sergey Levine,et al.  Learning force-based manipulation of deformable objects from multiple demonstrations , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Ross A. Knepper,et al.  DeepMPC: Learning Deep Latent Features for Model Predictive Control , 2015, Robotics: Science and Systems.

[20]  Petter Ögren,et al.  An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties , 2016, IEEE Transactions on Robotics.

[21]  Andrea Lockerd Thomaz,et al.  Simultaneously learning actions and goals from demonstration , 2016, Auton. Robots.

[22]  Joanna Bryson,et al.  A modular approach to learning manipulation strategies from human demonstration , 2016, Auton. Robots.

[23]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[24]  Bin Wei,et al.  A review on model reference adaptive control of robotic manipulators , 2017, Annu. Rev. Control..

[25]  Nolan Wagener,et al.  Information theoretic MPC for model-based reinforcement learning , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Steven L. Brunton,et al.  Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers , 2017, ArXiv.

[27]  C. Karen Liu,et al.  Deep Haptic Model Predictive Control for Robot-Assisted Dressing , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Jan Peters,et al.  Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations , 2018, IEEE Robotics and Automation Letters.

[29]  Sergey Levine,et al.  Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Sergey Levine,et al.  QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation , 2018, CoRL.

[31]  Yuanyuan Shi,et al.  Optimal Control Via Neural Networks: A Convex Approach , 2018, ICLR.