Skip to main navigation menu Skip to main content Skip to site footer

Peer Reviewed Article

Vol. 2 No. 01 (2026): DSJEM

Robotic Artificial Intelligence and Machine Learning

Submitted
March 19, 2026
Published
June 21, 2026

Abstract

Robotic Artificial Intelligence and Machine Learning are rapidly transforming capabilities and applications of various autonomous systems pretty quickly nowadays. Machines now perceive environments keenly with AI techniques like computer vision and decision-making algorithms integrated with robotic platforms and learn rapidly. Machine learning especially deep learning techniques enables robots perform complex tasks like object recognition and autonomous navigation with remarkably high precision. Technologies are being rapidly deployed across diverse domains like manufacturing healthcare and defense leading to markedly improved efficiency and safety. Robotics paired with AI/ML markedly boosts machine intelligence and pushes boundaries of achievement paving way for robots collaborating quite seamlessly with humans. Ongoing research endeavors rather zealously focus on bolstering generalization capabilities and real-time learning mechanisms for ethically deploying robotic intelligence sustainably into various societal frameworks.

References

  1. Jeong H., Lee H., Kim C. & Shin S. (2024)
  2. A Survey of Robot Intelligence with Large Language Models. Discusses how LLMs (e.g., RT-2) are transforming robotic perception and control pipelines, especially for multimodal embodied AI Wikipedia+15MDPI+15arXiv+15.
  3. Xu Z., Wu K., Wen J., Li J., Liu N., Che Z. & Tang J. (2024)
  4. A Survey on Robotics with Foundation Models: Toward Embodied AI. Covers integration of foundation models into navigation, planning, and manipulation modules with real-world relevance arXiv+1arXiv+1.
  5. Xiao X., Liu J., Wang Z., Zhou Y., Qi Y., Cheng Q., He B. & Jiang S. (2023)
  6. Robot Learning in the Era of Foundation Models: A Survey. Provides a comprehensive evaluation of generalization, simulators, and data efficiency in robotic learning systems arXiv.
  7. Pierson H. A. &Gashler M. S. (2017)
  8. Deep Learning in Robotics: A Review of Recent Research. One of the foundational surveys on deep neural networks in robotic perception, control, and manipulation arXiv.
  9. Fabisch A., Petzoldt C., Otto M. & Kirchner F. (2019)
  10. A Survey of Behaviour Learning Applications in Robotics. Focuses on real robotic behaviour learning using reinforcement and imitation learning arXiv.
  11. Leslie P. Kaelbling — Pioneer in reinforcement learning applied to robotics, especially POMDPs and learning behaviours under uncertainty in real-world domains Wikipedia.
  12. Pieter Abbeel — Leader in deep reinforcement and apprenticeship learning for robotic control, including manipulation learning from demonstration and autonomous policy acquisition Wikipedia.
  13. Aude Billard — Known for learning dexterous motor skills from demonstrations using biologically inspired dynamical systems, soft robotics, and imitation learning techniques Wikipedia.

Downloads

Download data is not yet available.

Similar Articles

1-10 of 21

You may also start an advanced similarity search for this article.