  A good estimation of the actions' cost is key in task planning for
human-robot collaboration. The duration of an action depends on agents'
capabilities and the correlation between actions performed simultaneously by
the human and the robot. This paper proposes an approach to learning actions'
costs and coupling between actions executed concurrently by humans and robots.
We leverage the information from past executions to learn the average duration
of each action and a synergy coefficient representing the effect of an action
performed by the human on the duration of the action performed by the robot
(and vice versa). We implement the proposed method in a simulated scenario
where both agents can access the same area simultaneously. Safety measures
require the robot to slow down when the human is close, denoting a bad synergy
of tasks operating in the same area. We show that our approach can learn such
bad couplings so that a task planner can leverage this information to find
better plans.
