mlsanityData to 7 Oct 2026

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

arXiv 2502.02692 ↗First posted 4 Feb 2025cs.RO cs.CV cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 47 of its 76 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

47 of 76 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perceptioncited by 7
  2. 2026VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluationcited by 7
  3. 2025Calibrated Decomposition of Aleatoric and Epistemic Uncertainty in Deep Features for Inference-Time Adaptationcited by 6
  4. 2026TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Samecited by 3
  5. 2025Edge Intelligence with Spiking Neural Networkscited by 2