Learning to Grasp Unknown Objects using Force Feedback

Koh, Keng Huat; Farhan, Musthafa; Liu, Yan Fei; Chan, Florence Hiu Ling; Lai, King Wai Chiu

doi:10.1109/cyber.2017.8446512

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Machine Learning Techniques1

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2023

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“…The RL framework made use of simulations to speed up the learning process, whereas the real gripper was a two-finger electric one. Another RL structure was implemented in [77] for a stepper-motor-driven gripper equipped with eight commercial force sensors. A stable grasp was first defined for a certain object and it was set as the objective state of the grasping adaptation procedure.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Control Methodologies for Robotic Grippers: A Review

Cortinovis,

Vitrani,

Maggiali

et al. 2023

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As automation is spreading in all the industry domains, the presence of robots is becoming unavoidable inside factories, warehouses and manufacturing facilities. Although a great number of companies and research institutions have concentrated their efforts on developing new robotic systems and advanced algorithms, much work is necessary to provide robotic grippers, especially industrial ones, with reliable, powerful control strategies. Therefore, this article aims at delivering an up-to-date point of view on the state of the art of robotic gripper control. The principal control methodologies employed so far, as well as a thorough selection of the existing contributions to the field, will be reported and discussed. Finally, the authors’ opinion about future directions will be expressed.

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Section: Machine Learning Techniquesmentioning

confidence: 99%