智能感知到行动：边缘端强大自治的机会与挑战

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

在机器人技术、智慧城市和自动驾驶等领域，自主边缘计算依赖于感知、处理和执行的无缝集成，以实现在动态环境中的实时决策。其核心是感知到执行的循环，通过迭代地将传感器输入与计算模型对齐，驱动自适应控制策略。这些循环可以适应超局部条件，提高资源效率和响应性，但也面临资源约束、多模态数据融合中的同步延迟以及反馈循环中级联错误的风险等挑战。本文探讨了如何通过主动的、上下文感知的感知到执行和执行到感知的调整来增强效率，根据任务需求动态调整感知和计算，例如感知环境的极小部分并预测其余部分。通过通过控制行动引导感知，执行到感知路径可以提高任务相关性和资源利用率，但也需要强大的监控来防止级联错误并保持可靠性。多智能体感知-执行循环通过协调分布式智能体的感知和行动进一步扩展了这些能力，通过协作优化资源使用。此外，受生物系统启发，神经形态计算提供了一种高效的基于脉冲的事件驱动处理框架，节约能量、减少延迟，并支持分层控制，使其成为多智能体优化的理想选择。本文强调了端到端共同设计策略的重要性，将算法模型与硬件和环境动态相一致，并改善跨层次相互依赖关系，以提高在复杂环境中的能效边缘自主性的吞吐量、精度和适应性。

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.