Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions

TL;DR

This paper reviews recent advances in neuromorphic computing for autonomous systems, emphasizing algorithms, hardware, and perception methods that improve energy efficiency, robustness, and real-time decision-making.

Contribution

It provides a comprehensive survey of current neuromorphic approaches, highlighting new methods and challenges for deploying neuromorphic systems in real-world autonomous applications.

Findings

Event-based vision sensors enable fast perception.

Neuromorphic algorithms improve energy efficiency.

Integration of spiking neural networks enhances robustness.

Abstract

The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving interest in neuromorphic computing. By drawing inspiration from biological neural systems, neuromorphic approaches offer promising pathways to enhance the perception, decision-making, and responsiveness of autonomous platforms. This paper surveys recent progress in neuromorphic algorithms, specialized hardware, and cross-layer optimization strategies, with a focus on their deployment in real-world autonomous scenarios. Special attention is given to event-based dynamic vision sensors and their role in enabling fast, efficient perception. The discussion highlights new methods that improve energy efficiency, robustness, adaptability, and reliability through the integration of spiking neural networks into autonomous system architectures. We integrate perspectives from machine learning, robotics, neuroscience, and neuromorphic engineering to offer a comprehensive view of the state of the field. Finally, emerging trends and open challenges are explored, particularly in the areas of real-time decision-making, continual learning, and the development of secure, resilient autonomous systems.