Active Perception with Neural Networks

TL;DR

This paper reviews recent progress in using neural networks for active perception in robotics, emphasizing real-time interpretation and the perception-action loop in single and multi-agent systems.

Contribution

It provides a comprehensive overview of how neural networks enhance active perception, focusing on real-time data interpretation and decision-making in robotic systems.

Findings

Neural networks enable semantic-level understanding of perception data.

Deep learning facilitates real-time perception-action loops.

Progress in multi-agent active perception systems.

Abstract

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.