Learning Curves of Skill Acquisition in Virtual Environments for Basic Operation of Harvesters and Forwarders

The need for safe, efficient, and standardized training methods has promoted the use of virtual reality simulators as an alternative or complementary tool to conventional training, especially in high-risk operational industries, such as forest operations. In this study, the learning process and the acquisition of technical skills were evaluated using individual and group learning curves, with task execution time as the main performance indicator. Ten trainees with no previous experience in forestry machinery voluntarily participated in basic scenarios using a Komatsu harvester (gripping and felling) and forwarder (loading and unloading) simulator. The aim was to develop basic operational skills, promote familiarization and adaptation, and train control commands. The training consisted of ten trials per trainee distributed across six scenarios with varying levels of complexity and duration. Exponential learning models were used to describe performance evolution, as they best fit the data, demonstrating that learning based on task execution time is nonlinear. The results showed a rapid and steep initial improvement followed by a phase of deceleration and progressive stabilization. Scenarios with greater complexity or longer execution times exhibited slower stabilization and higher inter-individual variability, suggesting increased cognitive and psychomotor demands, while scenarios with shorter execution times showed faster adaptation with less dispersion. All six scenarios showed a consistent pattern of learning, with a progressive decrease in execution time across trials. These findings support the integration of virtual reality technologies–especially simulators– into training programs and highlight the use of learning curves as a tool to evaluate performance improvements.