Online Learning of Human Constraints from Feedback in Shared Autonomy
Shibei Zhu Tran Nguyen Le Samuel Kaski Ville Kyrki
Real-time collaboration with humans poses challenges due to the different
behavior patterns of humans resulting from diverse physical constraints.
Existing works typically focus on learning safety constraints for
collaboration, or how to divide and distribute the subtasks between the
participating agents to carry out the main task. In contrast, we propose to
learn a human constraints model that, in addition, considers the diverse
behaviors of different human operators. We consider a type of collaboration in
a shared-autonomy fashion, where both a human operator and an assistive robot
act simultaneously in the same task space that affects each other's actions.
The task of the assistive agent is to augment the skill of humans to perform a
shared task by supporting humans as much as possible, both in terms of reducing
the workload and minimizing the discomfort for the human operator. Therefore,
we propose an augmentative assistant agent capable of learning and adapting to
human physical constraints, aligning its actions with the ergonomic preferences
and limitations of the human operator.