Authors:
Abhishek Padalkar
1
;
Matthias Nieuwenhuisen
2
;
Sven Schneider
3
and
Dirk Schulz
2
Affiliations:
1
Cognitive Mobile Systems Group, Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany, Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, St. Augustin, Germany
;
2
Cognitive Mobile Systems Group, Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany
;
3
Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, St. Augustin, Germany
Keyword(s):
Compliant Manipulation, Reinforcement Learning, Task Frame Formalism.
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.