On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks
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Abstract (arXiv)
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 validating 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 40 references that are in the map. First posted on arXiv 25 Sep 2024.
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References · 13
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 882
- 2018Residual Reinforcement Learning for Robot Controlcited by 242
- 2022Factory: Fast Contact for Robotic Assemblycited by 49
- 2023IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Realitycited by 44
- 2019Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assemblycited by 33
- 2024SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Taskscited by 12
- 2021Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Taskscited by 10
- 2020Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulationcited by 7
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