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Learning to Close the Gap: Combining Task Frame Formalism and Reinforcement Learning for Compliant Vegetable Cutting

  • Compliant manipulation is a crucial skill for robots when they are supposed to act as helping hands in everyday household tasks. Still, nowadays, those skills are hand-crafted by experts which frequently requires labor-intensive, manual parameter tuning. Moreover, some tasks are too complex to be specified fully using a task specification. Learning these skills, by contrast, requires a high number of costly and potentially unsafe interactions with the environment. We present a compliant manipulation approach using reinforcement learning guided by the Task Frame Formalism, a task specification method. This allows us to specify the easy to model knowledge about a task while the robot learns the unmodeled components by reinforcement learning. We evaluate the approach by performing a compliant manipulation task with a KUKA LWR 4+ manipulator. The robot was able to learn force control policies directly on the robot without using any simulation.

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Metadaten
Document Type:Conference Object
Language:English
Author:Abhishek Padalkar, Matthias Nieuwenhuisen, Sven Schneider, Dirk Schulz
Parent Title (English):Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2020, July 7-9, 2020
Pagenumber:11
First Page:221
Last Page:231
ISBN:978-989-758-442-8
DOI:https://doi.org/10.5220/0009590602210231
Publisher:SciTePress
Date of first publication:2020/07/15
Tag:Compliant Manipulation; Task Frame Formalism; reinforcement learning
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2020/07/22