Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration
This work addresses task planning efficiency for human-robot collaboration, but it is incremental as it builds on existing cost estimation methods with a focus on synergy effects.
The paper tackles the problem of estimating action costs in task planning for human-robot collaboration by learning action durations and synergy coefficients from past executions, showing that the approach can identify bad couplings to improve plans in simulated scenarios.
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.