mlsanityData to 14 Oct 2026

Machine Learning Meets Advanced Robotic Manipulation

arXiv 2309.12560 ↗First posted 22 Sep 2023cs.RO cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 121 of its 341 references that are in the map. First posted on arXiv 22 Sep 2023.

Builds on

121 of 341 references.

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50 most cited of 121 All 121 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Surveycited by 69
  2. 2023Value of Assistance for Graspingcited by 0