A Human-Aware Method to Plan Complex Cooperative and Autonomous Tasks using Behavior Trees

This paper proposes a novel human-aware method that generates robot plans for autonomous and human-robot cooperative tasks in industrial environments. We modify the standard Behavior Trees (BTs) formulation in order to take into account the action-related costs, and design suitable metrics and cost functions to account for the cooperation with a worker considering human availability, decisions, and ergonomics. The developed approach allows the robot to online adapt its plan to the human partner, by choosing the tasks that minimize the execution cost(s). Through simulations, we first tuned the weights of the cost function for a realistic scenario. Subsequently, the developed method is validated through a proofof-concept experiment representing the boxing of 4 different objects. The results show that the proposed cost-based BTs, along with the defined costs, enable the robot to online react and plan new tasks according to the dynamic changes of the environment, in terms of human presence and intentions. Our results indicate that the proposed solution demonstrates high potential in increasing robot reactivity and flexibility while, at the same time, in optimizing the decision-making process according to human actions.