Human–Machine Collaboration with Reinforcement Learning for Explaining Manual Layup Strategies in Composite Materials Handling

Accurately capturing and explaining material handling strategies in civil engineering projects could help to increase safety, quality, timely delivery, as well as understanding the material waste caused by rework and environmental condition changes, especially when humans are highly involved in tasks with complex geometries. Workers must adapt their strategies based on the working environment’s characteristics, leading to substantial differences in strategies depending on context which drives downstream inefficiencies. A particularly challenging area is the handling of composite materials, as they require substantial manual forming which is inherently error prone. This work seeks to model and understand the composite material handling process. Specifically, this paper aims to enable a reinforcement learning (RL) agent to emulate humans’ adaptive material handling process through layup process monitoring and control strategies. The taxonomy system of edge conditions and actions in the paper contributes to the description of the module configuration and controls the RL model with a small search space. Predictions of the policies including the layup sequences and action are utilized by the students to perform a manual layup on the module. Thus, integration of machine-predicted strategies into human operations shows an example of human–machine collaboration, bridging the gap between human intuition and machine’s predictions. The mean total shear, wrinkled area, and total fold were reduced from 56.31% to 50.37%, 21.07% to 17.74%, and 23.80% to 22.66%, respectively, through human–machine collaboration. The policy can also be transferred to different module configurations in future works to enhance humans’ work to adapt to various environments.