具身神经形态人工智能用于机器人技术：视角、挑战与研究发展栈
Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
AI总结：
本文针对机器人技术缺乏具身智能的问题，提出通过神经形态AI（SNN）实现具身智能的六个视角，涵盖学习规则、跨层优化、基准、可靠性、安全隐私及协同发展，并指出挑战与未来方向。
摘要：
机器人技术一直是提高人类生产力的不可或缺的组成部分，因为它们帮助人类以快速、准确且高效的方式完成各种复杂且密集的任务。因此，机器人技术已被部署在广泛的应用中，从个人使用到工业用例。然而，当前的机器人技术及其计算范式仍然缺乏具身智能，无法高效地与操作环境交互、做出正确/预期的响应动作，并适应环境变化。为此，神经形态计算与脉冲神经网络（SNN）的最新进展已展示出通过模拟生物大脑工作方式的生物合理计算范式（称为“神经形态人工智能（AI）”）来实现机器人具身智能的潜力。然而，基于神经形态AI的机器人领域仍处于早期阶段，因此其开发与部署以解决现实世界问题在多个设计方面暴露了新的挑战，例如准确性、适应性、效率、可靠性和安全性。为了解决这些挑战，本文将通过我们的视角讨论如何为机器人系统实现具身神经形态AI：（P1）基于有效学习规则、训练机制和适应性的具身智能；（P2）用于节能神经形态计算的跨层优化；（P3）具有代表性和公平性的基准；（P4）低成本可靠性和安全性增强；（P5）神经形态计算的安全与隐私；（P6）协同发展以实现节能且鲁棒的基于神经形态的机器人技术。此外，本文识别了研究挑战和机遇，并阐述了我们对未来研究发展的愿景，以推动机器人具身神经形态AI的发展。
英文摘要：
Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as "neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
