@article {pub-5378,
	title = {Learning Preferences for Intention-Aware Physical Human-Robot Cooperation},
	author = {Linda Spaa, van der AND Jens Kober AND Michael Gienger},
	year = {2024},
	month = {June},
	abstract = {The advent of collaborative robots allows humans and robots to cooperate in a direct and phys-
ical way. While this leads to amazing new opportunities to create novel robotics applications,
it is challenging to make the collaboration intuitive for the human. From a system{\textquoteright}s perspec-
tive, understanding the human intentions seems to be one promising way to get there. However,
human behavior exhibits large variations between individuals, such as for instance preferences
or physical abilities. This paper presents a novel concept for simultaneously learning a model
of the human intentions and preferences incrementally during the collaboration with a robot.
Starting out with a nominal model, the system acquires collaborative skills step-by-step within
only very few trials. The concept is based on a combination of model-based reinforcement learn-
ing and inverse reinforcement learning, adapted to fi t collaborations in which human and robot
think and act independently. We test the method and compare it to a baseline which imitates
the human, in both simulation and in a user study with a Franka Emika Panda robot arm.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	url = {https://link.springer.com/article/10.1007/s10514-024-10167-3?utm_source=rct_congratemailt\&utm_medium=email\&utm_campaign=oa_20240604\&utm_content=10.1007\%2Fs10514-024-10167-3},
	howpublished = {Open Access},
	journal = {Autonomous Robots},
	volume = {48}
}