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Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting
KTH Royal Institute of Technology, Stockholm, Sweden.
Chalmers University of Technology , Gothenburg, Sweden; KTH Royal Institute of Technology, Stockholm, Sweden.
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-3958-6179
KTH Royal Institute of Technology, Stockholm, Sweden.
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.
Keywords [en]
Model Learning for Control, Deep Learning in Robotics and Automation, Learning and Adaptive Systems
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-78674OAI: oai:DiVA.org:oru-78674DiVA, id: diva2:1379386
Conference
IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, October 15-17, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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Stork, Johannes Andreas

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf