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

2025-02-04

AI Summary

Dynamic edge environments suffer from resource constraints, high synchronization latency across multimodal sensors, and error propagation due to delayed or inaccurate feedback. Method: This paper proposes a bidirectional perception–decision–execution closed-loop architecture tailored for edge agents. It introduces the novel “sensing-to-action + action-to-sensing” co-design paradigm, establishing a distributed multi-agent framework for joint perception-execution optimization, underpinned by algorithm–hardware–environment trilateral co-design. Key techniques include event-driven neuromorphic computing, context-aware adaptive sensing scheduling, asynchronous multimodal fusion, and cross-layer software–hardware co-optimization. Contribution/Results: Experiments demonstrate 3–5× reduction in edge energy consumption, inference latency below 10 ms, and significant improvements in task completion rate and error recovery capability. The framework exhibits strong generalization and robustness, validated in real-world robot navigation and vehicle–infrastructure cooperative driving scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Enhancing real-time decision-making in dynamic environments.
• Addressing resource constraints and synchronization delays.
• Optimizing multi-agent coordination for efficient edge autonomy.

Innovation

• Proactive context-aware sensing-to-action adaptation
• Multi-agent coordinated sensing and actions
• Neuromorphic computing for energy-efficient processing