Abstract
Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn the state of a surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. Reinforcement learning is one of the modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, reinforcement learning has been used both for solving robotic computer vision problems such as object detection, visual tracking and action recognition as well as robot navigation. The paper describes shortly the reinforcement learning technology and its use for computer vision and robot navigation problems.
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Notes
- 1.
\(IoU(\hat{y}, y) = \frac{|\hat{y} \cap y|}{|\hat{y} \cup y|}\).
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Acknowledgement
The work was supported by the Skoltech NGP Program No. 1-NGP-1567 “Simulation and Transfer Learning for Deep 3D Geometric Data Analysis” (a Skoltech-MIT joint project).
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Bernstein, A.V., Burnaev, E.V., Kachan, O.N. (2018). Reinforcement Learning for Computer Vision and Robot Navigation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_20
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