  Active perception has been employed in many domains, particularly in the
field of robotics. The idea of active perception is to utilize the input data
to predict the next action that can help robots to improve their performance.
The main challenge lies in understanding the input data to be coupled with the
action, and gathering meaningful information of the environment in an efficient
way is necessary and desired. With recent developments of neural networks,
interpreting the perceived data has become possible at the semantic level, and
real-time interpretation based on deep learning has enabled the efficient
closing of the perception-action loop. This report highlights recent progress
in employing active perception based on neural networks for single and
multi-agent systems.
