Authors:
Vincenzo Petrone
;
Enrico Ferrentino
and
Pasquale Chiacchio
Affiliation:
Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
Keyword(s):
Physical Robot-Environment Interaction, Artificial Intelligence, Impedance Control, Reinforcement Learning, Peg-in-Hole.
Abstract:
This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI’s role in enhancing robotic manipulators – key drivers in the Fourth Industrial Revolution – is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications – e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) – highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of val
idating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts.
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