2026-08-06T06:48:22Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/146502026-07-28T06:15:49Zcom_20.500.11984_1143com_20.500.11984_14090col_20.500.11984_1148

Human-assisted reinforcement learning and dynamic force patterns in contact-rich manipulation for robotic disassembly Available to Purchase Arana-Arexolaleiba, Nestor Serrano, Antonio Chrysostomou, Dimitrios Remanufacturing Disassembly Motion control Reinforcement learning Human–robot collaboration Contact-rich manipulation ODS 4 Educación de calidad ODS 8 Trabajo decente y crecimiento económico ODS 9 Industria, innovación e infraestructura ODS 12 Producción y consumo responsables Purpose The escalating rates of material consumption and energy usage, along with resulting waste, pose significant environmental challenges. Circular economy principles, remanufacturing and disassembly strategies emerge as potential solutions to mitigate these impacts. However, disassembly processes face a critical hurdle due to the inherent variability of products. The purpose of this study is to explore the potential of human-assisted reinforcement learning approaches to effectively manage this variability and improve the adaptability and performance of disassembly operations. Design/methodology/approach This paper investigates the application of reinforcement learning employed with robot–human interactions to address this variability in disassembly, particularly in contact-rich tasks. In addition, this paper proposes using an impedance controller augmented with overlaid force oscillation to mitigate contact forces and jamming occurrences during occluded contact-rich manipulations. Findings The findings indicate that manipulation generalization capabilities are enhanced when policies receive human hints regarding where to focus their actions. Employing force overlay types – such as Lissajous curves and spiral shapes – under different parameters significantly reduces contact forces caused by friction during disassembly. These overlays lead to up to 10% reduction in mean force magnitude, a 55% decrease in the occurrence of contact forces exceeding the established threshold and a 28% reduction in task completion time compared to non-overlay execution. Originality/value The paper’s value lies in its innovative integration of human hints into reinforcement learning frameworks, which significantly enhances the adaptability and performance of disassembly tasks. This approach not only addresses the limitations of previous methods but also demonstrates substantial improvements in efficiency and flexibility, making it highly applicable to industrial settings. 2026-07-27T11:27:19Z 2026-07-27T11:27:19Z 2025 1758-5791 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=200655 https://hdl.handle.net/20.500.11984/14650 eng © 2025 Emerald Emerald