Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories

The article examines current approaches to training robotic manipulators for executing complex tasks in dynamic and changing environments. It provides a comparative analysis of modern training methods, highlighting their advantages and disadvantages. Additionally, the paper outlines the typical areas in which these methods are applied. Particular attention is given to approaches that involve human instructors, self-learning, and reinforcement learning. Special emphasis is placed on training efficiency, robot adaptability to new conditions, human-robot interaction, and the transfer of skills from virtual training environments to the real world. Based on the analysis, the authors recommend imitation learning — specifically, the learning from demonstration approach — as it enables the rapid and safe transfer of skills from humans to robots without the need for task formalization. The article also highlights the challenges of adapting trained models to real-world conditions and ensuring effective human-robot collaboration. It identifies key challenges faced by modern robot training systems. Based on these challenges, the article offers recommendations for selecting optimal training strategies according to the specific task type and available resources.