Related papers: Machine Learning Meets Advanced Robotic Manipulation

Machine Learning Meets Advanced Robotic Manipulation

URL: http://arxiv.org/abs/2309.12560v1
Date: Fri, 22 Sep 2023 01:06:32 GMT
Title: Machine Learning Meets Advanced Robotic Manipulation
Authors: Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Chee Peng Lim, Kevin Kelly, Fernando Bello
Abstract summary: The paper reviews cutting edge technologies and recent trends on machine learning methods applied to real-world manipulation tasks. The rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue.
Score: 48.6221343014126
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works.

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