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

2025-07-24

AI Summary

To address the urgent demand for high intelligence, strong adaptability, and ultra-low power consumption in autonomous systems—including robots, UAVs, and autonomous vehicles—this work proposes an embodied intelligence-driven neuromorphic computing paradigm. Methodologically, it deeply integrates event-driven dynamic vision sensors with spiking neural networks (SNNs), establishing a co-optimized architecture spanning algorithms, hardware, and system layers, and implements a low-latency, high-robustness real-time perception–decision closed loop on dedicated neuromorphic hardware. Key contributions include: (1) the first deep coupling of SNNs with event cameras to enable continual learning and interference-resilient perception; (2) a neuromorphic software–hardware co-design that reduces energy consumption by over 80% in empirical measurements; and (3) real-world validation of millisecond-level response latency, long-term operational stability, and environment-adaptive decision-making—establishing a scalable technical pathway toward safe, reliable next-generation autonomous intelligent systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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.

Problem

• Enhancing autonomous systems' perception and decision-making with neuromorphic computing
• Improving energy efficiency and robustness using spiking neural networks
• Addressing challenges in real-time decision-making and continual learning

Innovation

• Neuromorphic algorithms for autonomous systems
• Event-based dynamic vision sensors
• Spiking neural networks integration