Contributo in atti di convegno, 2022, ENG, 10.1109/ETFA52439.2022.9921721

Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration

Samuele Sandrini; Marco Faroni; Nicola Pedrocchi

STIIMA-CNR - Institute of Intelligent Industrial Technologies and Systems, National Research Council of Italy; STIIMA-CNR - Institute of Intelligent Industrial Technologies and Systems, National Research Council of Italy; STIIMA-CNR - Institute of Intelligent Industrial Technologies and Systems, National Research Council of Italy

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.

IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1–6, Stoccarda, Germania, 6-9/09/2022

Keywords

Human-Robot Interaction, Task And Motion Planning, Task planning, Learning for task planning

CNR authors

Sandrini Samuele, Pedrocchi Nicola, Faroni Marco

CNR institutes

STIIMA – Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato