Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges

TL;DR

This paper discusses the integration of sensing, processing, and actuation in edge computing for autonomous systems, emphasizing adaptive, resource-efficient, and robust sensing-to-action loops enhanced by neuromorphic computing and multi-agent coordination.

Contribution

It introduces proactive, context-aware sensing-to-action strategies and highlights the role of neuromorphic hardware and multi-agent systems in improving autonomous edge performance.

Findings

Proactive sensing improves resource efficiency and responsiveness.

Neuromorphic computing reduces energy consumption and latency.

Multi-agent coordination enhances system robustness and scalability.

Abstract

Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making in dynamic environments. At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to drive adaptive control strategies. These loops can adapt to hyper-local conditions, enhancing resource efficiency and responsiveness, but also face challenges such as resource constraints, synchronization delays in multi-modal data fusion, and the risk of cascading errors in feedback loops. This article explores how proactive, context-aware sensing-to-action and action-to-sensing adaptations can enhance efficiency by dynamically adjusting sensing and computation based on task demands, such as sensing a very limited part of the environment and predicting the rest. By guiding sensing through control actions, action-to-sensing pathways can improve task relevance and resource use, but they also require robust monitoring to prevent cascading errors and maintain reliability. Multi-agent sensing-action loops further extend these capabilities through coordinated sensing and actions across distributed agents, optimizing resource use via collaboration. Additionally, neuromorphic computing, inspired by biological systems, provides an efficient framework for spike-based, event-driven processing that conserves energy, reduces latency, and supports hierarchical control--making it ideal for multi-agent optimization. This article highlights the importance of end-to-end co-design strategies that align algorithmic models with hardware and environmental dynamics and improve cross-layer interdependencies to improve throughput, precision, and adaptability for energy-efficient edge autonomy in complex environments.