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 area...
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| Format: | Article |
| Language: | English |
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Lviv Polytechnic National University
2025-07-01
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| Series: | Energy Engineering and Control Systems |
| Subjects: | |
| Online Access: | https://science.lpnu.ua/jeecs/all-volumes-and-issues/volume-11-number-1-2025/analysis-methods-training-robotic-manipulators |
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| _version_ | 1849317752942100480 |
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| author | Yurii Senchuk Fedir Matiko |
| author_facet | Yurii Senchuk Fedir Matiko |
| author_sort | Yurii Senchuk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4d82720a4e7c4a5b9c116d78aac4be8d |
| institution | Kabale University |
| issn | 2411-8028 2415-7287 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Lviv Polytechnic National University |
| record_format | Article |
| series | Energy Engineering and Control Systems |
| spelling | doaj-art-4d82720a4e7c4a5b9c116d78aac4be8d2025-08-20T03:51:08ZengLviv Polytechnic National UniversityEnergy Engineering and Control Systems2411-80282415-72872025-07-01111536110.23939/jeecs2025.01.053Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories Yurii Senchuk0Fedir Matiko1Lviv Polytechnic National UniversityLviv Polytechnic National UniversityThe 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.https://science.lpnu.ua/jeecs/all-volumes-and-issues/volume-11-number-1-2025/analysis-methods-training-robotic-manipulatorsroboticsrobotic manipulatorteaching methodsadaptability |
| spellingShingle | Yurii Senchuk Fedir Matiko Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories Energy Engineering and Control Systems robotics robotic manipulator teaching methods adaptability |
| title | Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories |
| title_full | Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories |
| title_fullStr | Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories |
| title_full_unstemmed | Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories |
| title_short | Analysis of Methods for Training Robotic Manipulators to Perform Complex Motion Trajectories |
| title_sort | analysis of methods for training robotic manipulators to perform complex motion trajectories |
| topic | robotics robotic manipulator teaching methods adaptability |
| url | https://science.lpnu.ua/jeecs/all-volumes-and-issues/volume-11-number-1-2025/analysis-methods-training-robotic-manipulators |
| work_keys_str_mv | AT yuriisenchuk analysisofmethodsfortrainingroboticmanipulatorstoperformcomplexmotiontrajectories AT fedirmatiko analysisofmethodsfortrainingroboticmanipulatorstoperformcomplexmotiontrajectories |