CRII: RI: Building A Self-Learning Robot System with Neuromorphic Computing

在动物中，联想记忆是一种众所周知的能力，它能够有效地学习同时发生的事件之间的关系。相比之下，目前的人工智能系统缺乏类似的自我学习能力，而是依赖于从大量标签数据中学习。在数据、电力和其他资源有限的情况下，这种学习模式尤其具有挑战性。该项目将通过使机器人以更像人脑的方式进行计算和学习来应对这些挑战，旨在实现高能效和自我学习能力。这项研究将使机器人更智能、更节能，并能够在资源有限的环境中独立运行。该项目还将为密歇根上半岛的大学生和高中生提供参与神经科学、机器人和人工智能的跨学科研究的机会。该项目通过开发一种神经形态机器人来应对人工智能和机器人领域中数据稀缺和能源效率的关键挑战，该机器人无需依赖标记的数据集和人类干预即可学习。首先，将开发一种神经形态机器人，它利用尖峰神经网络、神经组件和各种神经编码方案进行感知和导航。其次，将通过模仿联想记忆学习的过程来开发自我学习算法。本研究将探索联想记忆过程中信号通路的改变，以实现联想和记忆。这项研究有可能创造新的训练算法，将尖峰信号的传播修改为目标函数。通过复制在啮齿动物身上观察到的导航和学习任务，将在迷宫中评估神经形态机器人系统的自我学习能力和能源效率。这项关于自学习机器人的研究将大大有助于节能机器人的发展，减少它们的尺寸、重量和能源预算，同时增强它们的独立性和智能性。如果成功，该项目将引入一种新的自学方法，与数据驱动的人工智能方法不同，以应对数据稀缺和能源效率的挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Associative memory is a well-known ability in animals that enables efficient learning of relationships among concurrent events. In contrast, current artificial intelligence systems lack a comparable self-learning capability, relying instead on learning from vast amounts of labeled data. That mode of learning is particularly challenging in scenarios where data, power, and other resources are limited. This project will address these challenges by enabling robots to compute and learn in a manner more like human brains, aiming to achieve high energy efficiency and self-learning capability. This research will make the robot smarter, more energy efficient, and able to operate independently in resource-constrained environments. The project will also provide opportunities for college and high school students from the Upper Peninsula of Michigan to participate in interdisciplinary research on neuroscience, robotics, and artificial intelligence.The project addresses critical challenges of data scarcity and energy efficiency in artificial intelligence and robotics through the development of a neuromorphic robot that can learn without relying on labeled datasets and human intervention. First, a neuromorphic robot will be developed that utilizes spiking neural networks, neural assemblies, and various neural coding schemes for perception and navigation. Second, self-learning algorithms will be developed by mimicking the process of associative memory learning. The study will explore signal pathway modification during associative memory to implement associations and memorization. The research has the potential to create new training algorithms that modify the propagation of spiking signals as objective functions. The self-learning capability and energy efficiency of the neuromorphic robotic system will be evaluated in mazes by replicating navigation and learning tasks observed in rodents. This research on self-learning robotics will significantly benefit the development of energy-efficient robotics by reducing their size, weight, and energy budgets while enhancing their independence and intelligence. If successful, the project will introduce a new self-learning method, diverging from data-driven artificial intelligence approaches, to address the challenges of data scarcity and power efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.