@inproceedings{PadalkarNieuwenhuisenSchneideretal.2020, author = {Abhishek Padalkar and Matthias Nieuwenhuisen and Sven Schneider and Dirk Schulz}, title = {Learning to Close the Gap: Combining Task Frame Formalism and Reinforcement Learning for Compliant Vegetable Cutting}, series = {Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2020, July 7-9, 2020}, publisher = {SciTePress}, isbn = {978-989-758-442-8}, doi = {10.5220/0009590602210231}, pages = {221 -- 231}, year = {2020}, abstract = {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.}, language = {en} }