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

TL;DR

This paper analyzes how intelligent sensing-to-action loops at the edge can enable robust, low-latency autonomy in dynamic environments by tightly coupling sensing, processing, and actuation. It advocates end-to-end co-design across hardware, models, and environmental dynamics, with proactive strategies like generative sensing (R-MAE) to reduce sensing burden and Koopman-based action-to-sensing to optimize perception under control. The work also introduces reliability mechanisms (STARNet) to detect and mitigate cascading errors, and highlights neuromorphic and multi-agent frameworks for energy-efficient, scalable edge autonomy. Overall, the findings demonstrate substantial resource savings and improved resilience, guiding future hardware-software co-design for edge robotics, smart cities, and autonomous vehicles.

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.

Figures (11)

• Figure 1: Opportunities for Intelligent Sensing-to-Action: In sensing-to-action loops, significant gains can be achieved by selectively sensing critical environmental regions while predicting less critical areas based on training data. This frugal sensing strategy is especially beneficial for resource-intensive modalities, such as LiDAR, enhancing task accuracy without unnecessary overhead. Similarly, action-to-sensing optimizations can adjust control variables to opportunistically reduce sensing demands based on task relevance. While these frameworks improve loop efficiency, ensuring reliability requires robust and computationally efficient monitors to continuously assess fidelity and support aggressive optimizations. In multi-agent sensing-action loops, agents can collaborate by sharing sensing tasks or complementing each other's sensing capabilities. Moreover, emerging paradigms, such as neuromorphic sensing-action loops, offer unified frameworks by adapting sensing and processing rates based on event dynamics, enabling seamless sensing and control.
• Figure 2: An end-to-end computing pipeline comparison sensing-processing-action loop between a biological and a neuromorphic system. In a biological system, inputs are perceived as changes in intensity (events and frames) and color (frames) by the eye. In contrast, a neuromorphic system uses frame cameras to capture analog intensity at low rates and event cameras to detect motion-induced variations, generating events. The brain's parallel and recurrent connections enable computation within memory. Neuromorphic system emulates this by combining ANNs, SNNs, and hybrid ANN-SNN models to balance accuracy and efficiency. These algorithms also benefit from hardware acceleration via in-memory (IMC) and near-memory (NMC) computing by efficiently implementing synaptic functionality and, work alongside CPU/GPU architectures to enhance efficiency and reduce latency.
• Figure 3: Generative Sensing: Sense only what you really need: Generative sensing optimizes resource use by focusing on essential environmental features, reducing unnecessary data collection and enhancing real-time responsiveness. For LiDAR proessing, in this approach, the input point cloud is voxelized and radially masked based on voxel distance from the sensor to minimize redundant information. A 3D spatially sparse convolutional encoder extracts latent features, while a decoder reconstructs the 3D scene, enabling efficient perception that supports adaptive sensing-to-action strategies.
• Figure 4: Our approach conditions visual representations on the task policy by incorporating contrastive spectral Koopman encoding and reinforcement learning (RL)-guided control. This high-level framework unifies perception and control, enabling task-aware sensing adjustments. The RoboKoop model leverages these representations to dynamically adjust sensing parameters based on control objectives. (Adapted from RoboKoopkumawat2024robokoopefficientcontrolconditioned)
• Figure 5: (a) Computational load of state-of-the-art dynamical models. (b) Performance under external disturbances. (Adapted from RoboKoopkumawat2024robokoopefficientcontrolconditioned)