title: Intelligent Sensing-to-Action for Robust Autonomy at the Edge:
    Opportunities and Challenges
summary: 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.
paper_authors._id: 67aa915d2e821999a96f8d85
paper_authors.name: Amit Ranjan Trivedi
paper_authors._id: 67aa915d2e821999a96f8d86
paper_authors.name: Sina Tayebati
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:25:47.153Z
paper_authors._id: 655ec30b12fb73960ceb048f
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paper_authors.fullname: Sina Tayebati
paper_authors.type: user
paper_authors.user: sinatayebati
paper_authors._id: 67aa915d2e821999a96f8d87
paper_authors.name: Hemant Kumawat
paper_authors._id: 67aa915d2e821999a96f8d88
paper_authors.name: Nastaran Darabi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:23.540Z
paper_authors._id: 671acb0de80155d7f9e162b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/g7hnS2Mrjyy-RudyIxvVX.png
paper_authors.fullname: Nastaran Darabi
paper_authors.type: user
paper_authors.user: Nstrndrbi
paper_authors._id: 67aa915d2e821999a96f8d89
paper_authors.name: Divake Kumar
paper_authors._id: 67aa915d2e821999a96f8d8a
paper_authors.name: Adarsh Kumar Kosta
paper_authors._id: 67aa915d2e821999a96f8d8b
paper_authors.name: Yeshwanth Venkatesha
paper_authors._id: 67aa915d2e821999a96f8d8c
paper_authors.name: Dinithi Jayasuriya
paper_authors._id: 67aa915d2e821999a96f8d8d
paper_authors.name: Nethmi Jayasinghe
paper_authors._id: 67aa915d2e821999a96f8d8e
paper_authors.name: Priyadarshini Panda
paper_authors._id: 67aa915d2e821999a96f8d8f
paper_authors.name: Saibal Mukhopadhyay
paper_authors._id: 67aa915d2e821999a96f8d90
paper_authors.name: Kaushik Roy
paper_title: Intelligent Sensing-to-Action for Robust Autonomy at the Edge:
    Opportunities and Challenges
paper_summary: 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.
paper_ai_summary: Proactive and context-aware adaptations in sensing-to-action loops, combined with neuromorphic computing and multi-agent coordination, enhance efficiency and reliability in autonomous edge computing for dynamic environments.
paper_ai_keywords: neuromorphic computing spike-based processing event-driven processing hierarchical control multi-agent sensing-action loops