# Active Perception with Neural Networks

arXiv: https://arxiv.org/abs/2109.02744 · HTML: https://mlsanity.com/paper/2109.02744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Learning to Perceive in Deep Model-Free Reinforcement Learning](https://mlsanity.com/paper/2301.03730/) (2023)

## Abstract (arXiv)

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.