TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Padalkar, Abhishek
A1  - Nieuwenhuisen, Matthias
A1  - Schneider, Sven
A1  - Schulz, Dirk
T1  - Learning to Close the Gap: Combining Task Frame Formalism and Reinforcement Learning for Compliant Vegetable Cutting
T2  - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2020, July 7-9, 2020
N2  - 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.
KW  - Compliant Manipulation
KW  - Task Frame Formalism
KW  - reinforcement learning
SN  - 978-989-758-442-8
SB  - 978-989-758-442-8
U6  - https://doi.org/10.5220/0009590602210231
DO  - https://doi.org/10.5220/0009590602210231
SP  - 221
EP  - 231
S1  - 11
PB  - SciTePress
ER  -