Machine Learning Meets Advanced Robotic Manipulation
It addresses the challenge of automating complex manipulation tasks in industries like manufacturing and healthcare, but is incremental as it synthesizes existing research rather than presenting new findings.
This survey reviews machine learning methods for robotic manipulation tasks, aiming to improve safety, reliability, and efficiency in real-world applications across various domains.
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.