Abstract
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.
Abstract (translated)
机器人技术在提高人类生产力的过程中一直是一个不可或缺的部分，因为他们以快速、准确、高效的方式帮助人类完成各种复杂、密集的任务。因此，机器人技术已经在个人到工业应用范围内得到了广泛应用。然而，目前的机器人技术和计算范式仍然缺乏肢体智能，无法有效地与操作环境互动，对正确的/预期动作作出反应，并适应环境变化。为此，近年来关于神经形态计算（SNN）的神经网络的进步展示了通过类生物计算范式实现机器人肢体智能的可能性，这种范式模仿了生物大脑的工作方式，被称为“神经形态人工智能（AI）”。然而，基于神经形态人工智能的机器人领域仍然处于早期阶段，因此其开发和部署为解决现实问题暴露了在设计方面的新挑战，例如准确性、适应性、效率、可靠性和安全性。为应对这些挑战，本文将探讨通过我们的观点如何实现机器人系统中的肢体智能神经形态人工智能（AI）的方法：（P1）基于有效学习规则、训练机制和适应性的肢体智能；（P2）跨层优化实现能源高效的神经形态计算；（P3）具有代表性且公正的基准；（P4）低成本可靠性和安全增强；（P5）神经形态计算的安全和隐私；（P6）能源效率高和鲁棒性强的神经形态机器人发展。此外，本文还识别出研究挑战和机会，并阐述了我们对未来机器人研究开发中肢体智能神经形态人工智能的愿景。
URL
https://arxiv.org/abs/2404.03325
