Robotics plays a significant part in raising the standard of living. With a variety of useful applications in several service sectors, such as transportation, manufacturing, and healthcare. In order to make these services useable with efficacy and efficiency in having robotics obey the directions supplied to them by the program, continuous improvement is required. Intensive research has been focusing on the way to improve these services which has led to the use of sub-sections of artificial intelligence represented by ML and DL with their state-of-the-art algorithms and architecture adding positive improvements to the field of robotics. Recent studies prove various ML/DL algorithms for robotic system architectures to offer solutions for different issues related to, robotics autonomy, and decision making. This chapter provides a thorough review about autonomous and automatic robotics along with their uses. Additionally, the chapter discusses extensive machine learning techniques such as machine learning for robotics. And finally, a discussion about the issues and future of artificial intelligence applications in robotics.
- Robotics applications
- Machine learning
- Deep learning
- Visioning applications
- Assistive technology
- Imitation learning
- Soft robotics
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Alatabani, L.E., Ali, E.S., Saeed, R.A. (2023). Machine Learning and Deep Learning Approaches for Robotics Applications. In: Azar, A.T., Koubaa, A. (eds) Artificial Intelligence for Robotics and Autonomous Systems Applications. Studies in Computational Intelligence, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-031-28715-2_10
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