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Data-Driven Model Predictive Control for Food-Cutting
Division of Robotics, Perception and Learning (RPL), CAS, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
Division of Systems and Control, Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0003-3958-6179
Division of Robotics, Perception and Learning (RPL), CAS, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
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 extend on previous work that was limited to torque-controlled robots by incorporating Force/Torque sensor measurements and formulate the control problem so that it can be applied to the more common velocity controlled robots. 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.

Nationell ämneskategori
Robotteknik och automation Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:oru:diva-73149OAI: oai:DiVA.org:oru-73149DiVA, id: diva2:1296372
Tillgänglig från: 2019-03-15 Skapad: 2019-03-15 Senast uppdaterad: 2019-03-22Bibliografiskt granskad

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Stork, Johannes Andreas

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Stork, Johannes Andreas
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Institutionen för naturvetenskap och teknik
Robotteknik och automationDatavetenskap (datalogi)

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