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Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
Division of Systems and Control, Dept. of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.ORCID iD: 0000-0001-5129-342X
Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
National Category
Engineering and Technology Robotics
Identifiers
URN: urn:nbn:se:kth:diva-258796OAI: oai:DiVA.org:kth-258796DiVA, id: diva2:1351463
Conference
The 2019 IEEE-RAS International Conference on Humanoid Robots, Toronto, Canada, October 15-17, 2019.
Note

QC 20191021

Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-10-21Bibliographically approved

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