Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges
Amit Ranjan Trivedi, Sina Tayebati, Hemant Kumawat, Nastaran Darabi, Divake Kumar, Adarsh Kumar Kosta, Yeshwanth Venkatesha, Dinithi Jayasuriya, Nethmi Jayasinghe, Priyadarshini Panda, Saibal Mukhopadhyay, Kaushik Roy
2025-02-10
Summary
This paper talks about improving how robots and smart devices make decisions in real-time using a method called sensing-to-action loops. It explores ways to make these systems more efficient and responsive while dealing with challenges like limited resources and potential errors.
What's the problem?
Autonomous systems like robots and self-driving cars need to quickly process information from their surroundings and make decisions. However, they face issues such as limited computing power, delays in combining different types of data, and the risk of small errors snowballing into bigger problems.
What's the solution?
The researchers propose using smarter, more aware sensing-to-action loops that can adjust how they gather and process information based on what's needed for the task. They suggest only sensing a small part of the environment and predicting the rest, using actions to guide what to sense next, and having multiple devices work together. They also recommend using a type of computing inspired by how brains work to save energy and reduce delays.
Why it matters?
This matters because it could make robots and smart devices more efficient, responsive, and reliable in complex real-world situations. By improving how these systems handle information and make decisions, we could see better performance in areas like robotics, smart cities, and self-driving cars, all while using less energy and computing power.
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.