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Learning to close the gap: Combining task frame formalism and reinforcement learning for compliant vegetable cutting

: Padalkar, A.; Nieuwenhuisen, M.; Schneider, S.; Schulz, D.


Gusikhin, O.:
ICINCO 2020. Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics : July 7-9, 2020
Setubal: SciTePress, 2020
ISBN: 978-989-758-442-8
International Conference on Informatics in Control, Automation and Robotics (ICINCO) <17, 2020, Online>
Conference Paper
Fraunhofer FKIE ()

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.