智能感知至行動：邊緣強大自主性的機會與挑戰

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

February 4, 2025

作者: 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

cs.AI

摘要

在機器人學、智慧城市和自動駕駛領域，自主邊緣運算依賴於感知、處理和執行的無縫整合，以實現在動態環境中的即時決策。其核心是感知到行動的迴路，透過將感測器輸入與計算模型迭代地對齊，以驅動適應性控制策略。這些迴路可以適應超局部條件，增強資源效率和響應性，但也面臨著資源限制、多模態數據融合中的同步延遲，以及反饋迴路中串聯錯誤的風險等挑戰。本文探討了如何透過主動的、上下文感知的感知到行動和行動到感知適應，通過根據任務需求動態調整感知和計算，例如感知環境的極其有限部分並預測其餘部分，來增強效率。通過引導感知透過控制行動，行動到感知路徑可以提高任務相關性和資源使用，但也需要強大的監控以防止串聯錯誤並保持可靠性。多智能體感知行動迴路通過協調分佈式智能體之間的感知和行動，進一步擴展了這些能力，通過協作優化資源使用。此外，受生物系統啟發的神經形態運算提供了一個高效的基於脈衝的事件驅動處理框架，節省能源、降低延遲，並支持分層控制，使其成為多智能體優化的理想選擇。本文強調了端到端共同設計策略的重要性，通過將算法模型與硬體和環境動態相一致，改善跨層次相互依賴關係，以提高在複雜環境中的能源高效邊緣自主性的吞吐量、精度和適應性。

English

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.