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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 universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0003-3958-6179
KTH Royal Institute of Technology, Stockholm, Sweden.
2019 (engelsk)Inngår i: IEEE-RAS International Conference on Humanoid Robots, IEEE Computer Society, 2019, s. 244-250, artikkel-id 9035011Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2019. s. 244-250, artikkel-id 9035011
Serie
IEEE-RAS International Conference on Humanoid Robots, E-ISSN 2164-0580
Emneord [en]
Model Learning for Control, Deep Learning in Robotics and Automation, Learning and Adaptive Systems
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-78674DOI: 10.1109/Humanoids43949.2019.9035011ISI: 000563479900030Scopus ID: 2-s2.0-85082700744ISBN: 978-1-5386-7630-1 (digital)OAI: oai:DiVA.org:oru-78674DiVA, id: diva2:1379386
Konferanse
IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, October 15-17, 2019
Forskningsfinansiär
Swedish Foundation for Strategic Research , GMT14-0082 FACTKnut and Alice Wallenberg FoundationTilgjengelig fra: 2019-12-17 Laget: 2019-12-17 Sist oppdatert: 2020-09-16bibliografisk kontrollert

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Totalt: 200 treff
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